Searching social media content

ABSTRACT

Various embodiments provide for systems, methods, and computer-readable storage media that improve media content search functionality and curation of media content. For instance, various embodiments described in this document provide features that can present media content items in the form of dynamic collection of media content items upon a user typing into a search bar. In another instance, various embodiments described herein improve media content search functionality by ranking user facing search features using input signals.

RELATED APPLICATIONS

This application claims the benefit of priority under 35 USC 119(e) ofU.S. Provisional Patent Application No. 62/460,549 filed on Feb. 17,2017, and U.S. Provisional Patent Application No. 62/460,583 filed onFeb. 17, 2017, the contents of each being incorporated herein byreference in their entirety.

BACKGROUND

There are several ways in which social networks can leverage searchingfor social media content (e.g., picture or video content). In additionto providing users a means for searching and filtering through socialmedia content based on keywords (e.g., content relating to people,events and topics relevant to them), search features can permit a socialnetwork to create user views and dashboards, grouping of social mediacontent, curation of media social media content, and extraction oftopics from social media content.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate some embodimentsof the present disclosure and should not be considered as limiting itsscope. The drawings are not necessarily drawn to scale. To easilyidentify the discussion of any particular element or act, the mostsignificant digit or digits in a reference number refer to the figurenumber in which that element is first introduced, and like numerals maydescribe similar components in different views.

FIG. 1 is block diagram illustrating further details regarding amessaging system in accordance with some embodiments described in thisdocument.

FIGS. 2, 3A, and 3B are screenshots of example graphical userinterfaces, each of which presents a pre-typing view in accordance withsome embodiments described in this document.

FIGS. 4-12 are screenshots of example graphical user interfaces thateach present a section of a pre-typing view in accordance with someembodiments described in this document.

FIGS. 13-15 are screenshots of example graphical user interfaces thateach present a section of a post-typing view in accordance with someembodiments described in this document.

FIG. 16 are screenshots of example graphical user interfaces that eachpresent a view of post-typing view in accordance with some embodimentsdescribed in this document.

FIGS. 17-25 are screenshots of example graphical user interfaces thateach present a vertical section of a post-typing view based on a searchquery in accordance with some embodiments described in this document.

FIGS. 26A and 26B are screenshots of example graphical user interfacesthat each present a post-typing view under different search scenarios inaccordance with some embodiments described in this document.

FIGS. 27A-27D are screenshots of example graphical user interfaces thatpresent a post-typing view based on client-side caching in accordancewith some embodiments described in this document.

FIG. 28 are screenshots of example graphical user interfaces thatpresent different states of a post-typing view in accordance with someembodiments described in this document.

FIGS. 29-33 are diagrams illustrating example indexing infrastructure inaccordance with some embodiments described in this document.

FIG. 34 is a diagram illustrating an example serving infrastructure inaccordance with some embodiments described in this document.

FIGS. 35 and 36 are screenshots of example graphical user interfaces fora social media content curation tool in accordance with some embodimentsdescribed in this document.

FIG. 37 is a flow diagram illustrating an example request flow in asocial media content curation tool in accordance with some embodimentsdescribed in this document.

FIG. 38 is a flow diagram illustrating an example method for generatingdynamic collections of media content items in accordance with someembodiments described in this document.

FIGS. 39 through 43 are screenshots illustrating example graphical userinterfaces each of which presents a pre-typing view in accordance withsome embodiments described in this document.

FIG. 44 is a screenshot of an example graphical user interface thatpresents a section of a post-typing view in accordance with someembodiments described in this document.

FIG. 45 is a flow diagram illustrating an example method for generatingcollections of media content items in accordance with some embodimentsdescribed in this document.

FIG. 46 is a block diagram illustrating a representative softwarearchitecture in accordance with some embodiments described in thisdocument.

FIG. 47 is a block diagram illustrating components of a machine, inaccordance with some embodiments described in this document, able toread instructions from a machine-readable medium (e.g., amachine-readable storage medium) and perform any one or more of themethodologies discussed in this document.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Among other things, embodiments of the present disclosure improve mediacontent (e.g., social media content) search functionality and curationof media content (e.g., social media content). For instance, variousembodiments described in this document provide features that can presentmedia content items in the form of dynamic collection of media contentitems (e.g., stories) upon a user clicking and typing into a search bar.In another instance, various embodiments described herein improve mediacontent search functionality by ranking user facing search featuresusing input signals.

FIG. 1 is a block diagram showing an example of a messaging system 100for exchanging data (e.g., messages and associated content) over anetwork. The messaging system 100 includes multiple client devices 102,each of which hosts a number of applications including a messagingclient application 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet). As used in this document, the term “client device”may refer to any machine that interfaces to a communications network(such as network 106) to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

In the example shown in FIG. 1, each messaging client application 104 isable to communicate and exchange data with another messaging clientapplication 104 and with the messaging server system 108 via the network106. The data exchanged between messaging client applications 104, andbetween a messaging client application 104 and the messaging serversystem 108, includes functions (e.g., commands to invoke functions) aswell as payload data (e.g., text, audio, video or other multimediadata).

The network 106 may include, or operate in conjunction with, an ad hocnetwork, an intranet, an extranet, a virtual private network (VPN), alocal area network (LAN), a wireless LAN (WLAN), a wide area network(WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), theInternet, a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a plain old telephone service (POTS) network,a cellular telephone network, a wireless network, a Wi-Fi® network,another type of network, or a combination of two or more such networks.For example, a network or a portion of a network may include a wirelessor cellular network and the coupling may be a Code Division MultipleAccess (CDMA) connection, a Global System for Mobile communications(GSM) connection, or other type of cellular or wireless coupling. Inthis example, the coupling may implement any of a variety of types ofdata transfer technology, such as Single Carrier Radio TransmissionTechnology (1×RTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (GPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long TermEvolution (LTE) standard, others defined by various standard settingorganizations, other long range protocols, or other data transfertechnology.

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described in thisdocument as being performed by either a messaging client application 104or by the messaging server system 108, it will be appreciated that thelocation of certain functionality either within the messaging clientapplication 104 or the messaging server system 108 is a design choice.For example, it may be technically preferable to initially deploycertain technology and functionality within the messaging server system108, but to later migrate this technology and functionality to themessaging client application 104 where a client device 102 has asufficient processing capacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include, message content, client device information,geolocation information, media annotation and overlays, message contentpersistence conditions, social network information, and live eventinformation, as examples. Data exchanges within the messaging system 100are invoked and controlled through functions available via userinterfaces (UIs) of the messaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the Application Program Interface (API) server110, this server receives and transmits message data (e.g., commands andmessage payloads) between the client device 102 and the applicationserver 112. Specifically, the Application Program Interface (API) server110 provides a set of interfaces (e.g., routines and protocols) that canbe called or queried by the messaging client application 104 in order toinvoke functionality of the application server 112. The ApplicationProgram Interface (API) server 110 exposes various functions supportedby the application server 112, including account registration, loginfunctionality, the sending of messages, via the application server 112,from a particular messaging client application 104 to another messagingclient application 104, the sending of electronic media files (e.g.,electronic images or video) from a messaging client application 104 tothe messaging server application 114, and for possible access by anothermessaging client application 104, the setting of a collection of mediacontent items (e.g., story), the retrieval of a list of friends of auser of a client device 102, the retrieval of such collections, theretrieval of messages and content, the adding and deletion of friends toa social graph, the location of friends within a social graph, openingand application event (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including a messaging server application 114, an imageprocessing system 116, a social network system 122, a search system 124,and a ranking system 126. The messaging server application 114implements a number of message processing technologies and functions,particularly related to the aggregation and other processing of content(e.g., textual and multimedia content including images and video clips)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of media content items (e.g., called stories or galleries).These collections are then made available, by the messaging serverapplication 114, to the messaging client application 104. Otherprocessor and memory intensive processing of data may also be performedserver-side by the messaging server application 114, in view of thehardware requirements for such processing.

The application server 112 also includes an image processing system 116that is dedicated to performing various image processing operations,typically with respect to electronic images or video received within thepayload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions services, and makes these functions and services available tothe messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 within thedatabase 120. Examples of functions and services supported by the socialnetwork system 122 include the identification of other users of themessaging system 100 with which a particular user has relationships oris “following”, and also the identification of other entities andinterests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

Some embodiments may include one or more wearable devices, such as apendant with an integrated camera that is integrated with, incommunication with, or coupled to, a client device 102. Any desiredwearable device may be used in conjunction with the embodiments of thepresent disclosure, such as a watch, eyeglasses, goggles, a headset, awristband, earbuds, clothing (such as a hat or jacket with integratedelectronics), a clip-on electronic device, or any other wearabledevices.

The search system 124 performs search operations on social media contentas described and illustrated in this document.

According to various embodiments, a search feature presents the socialmedia search results as cards that appear in a view of a graphical userinterface (“user interface”). In particular, the search feature cancomprise a pre-typing view, post-typing view, or both. The pre-typingview appears on a user interface in the absence of user parameters forexecuting a social media search (e.g., a search box displayed on theuser interface is left blank), while a post-typing view appears on theuser interface in the presence of a user parameter for executing asocial media search (e.g., appears as soon as the user begins to entercharacters of a search query into the search box). The user interfacemay be configured such that when the post-typing view appears, thepre-typing view is replaced by the post-typing view, and when thepre-typing view appears, the post-typing view is replaced by thepost-typing view. Social media search results presented in the userinterface may be consistent across different types of searches (e.g.,camera, stories, chat, live stories, events, etc.).

The pre-typing view can comprise such views as: special occasion(birthdays, breaking news, etc.) cards view; an emoji carousel view; anenhanced quick chat carousel view; a topics carousel view; a livestories carousel view; an around me carousel view; media content item(e.g., a digital photo, image, video, etc.) tabs view that can include,for example, highlights, shows, sports, fashion, pets, attractions, andneighborhoods; a discover carousel view; user tabs view; or acombination thereof. The pre-typing goal can, for example, facilitatequick access to best friends on the social network, and access to thebest social media relating to (e.g., media content items from) populartopics, events, and places.

As used herein, a snap may comprise a user-captured or user-enhanced(e.g., text, visual effects, etc.) digital photo or video. As also usedherein, a story or a gallery is a collection of media content items.

The post-typing view can comprise such views as: a search-as-you-typeview; a hero card best matching query view; a view showing the resultset based on disambiguation of a query; a view for more stories for herocard; a related searches view; a related people view; or somecombination thereof. The pre-typing goal can, for example: bucket thebest social media (e.g., media content items) into the Hero card;surface Hero cards as quickly as possible while typing; focus on goodexperiences within core verticals (specific places, people, topics,broad places, events, and categories); and provide a path to moreresults/similar searches if available.

The ranking system 126 performs search operations on social mediacontent as described and illustrated in this document.

According to various embodiments, ranking social media content asdescribed in this document can be utilized with: dynamic storygeneration; emoji carousel generation; topics generation and ranking;live stories ranking; post-type search results; around me carouselgeneration; media content item tabs table generation; more storiessection generation; related search section generation; query suggestion;breaking news section generation; people searching; related peoplesection generation, and spam and abuse moderation.

Inputs that might be utilized by the ranking include, withoutlimitation: bucketed friend count; bucketed follower count; number ofmedia content items (e.g., snaps) sent over a period of time (e.g., last28 days); number of media content items received over a period of time(e.g., last 28 days); number of stories posted over a period of time(e.g., last 28 days); total number of views for stories posted over aperiod of time (e.g., last 28 days); total time of views for storiesposted over a period of time (e.g., last 28 days); bucketed number ofdays since account creation; location of the media content item; time ofthe media content item; caption of the media content item; whether amedia content item is a taken using the front-facing camera; and visuallabels associated with the media content item; story telling score ofthe media content item; creative quality score of a media content item;and media quality signals.

The visual labels associated with a media content item comprises a listof labels/annotations describing the objects in the media content item(e.g., both images and videos) using the trained visual annotationmodel. The story telling score of the media content item comprises ascore (e.g., between 0, 1) that is assigned to each media content item.The score may be a function of: number of media content items from otherusers posted around the time and location of the media content item;number of media content items from the same user posted around the timeand location of the media content item; the descriptiveness of the mediacontent item (e.g., whether it is a video and whether it has caption);and the visual representativeness of media content item given the mediacontent items coming from the same location. The creative quality scoreof a media content item comprises a score (e.g., between 0 and 1) thatis a function of the normalized view time per view on a user's “RecentUpdates” media content items combined with the total number of views onthe user's “Recent Updates” media content items. All “Recent Updates”stats may be computed based on a period of time (e.g., last 28 days ofdata). The media quality signals may comprise image media content itemsand video media content items. For image media content item, the mediaquality signals are the normalized mean of a media content item's grayscaled pixel values, the entropy of a media content item's gray scaledpixels, and the size of the media content item (in bytes). For videomedia content items, the media quality signals are the percentage oftraceable key point features across video frames, the percentage ofdifferences in pixel values in consecutive frames, and the percentage ofspikes of differences in pixel values in consecutive frames.

For some embodiments, dynamic stories are algorithmically-generatedstories built from media content items submitted to in real-time. Thismay be accomplished over multiple stages, as illustrated by FIG. 39.

FIG. 2 is a screenshot of an example graphical user interface thatpresents a pre-typing view in accordance with some embodiments describedin this document. As shown, the pre-typing view includes sections forquick chat, groups, new friends, quick add, and contacts. One or more ofthese sections can be populated by way of a search feature described inthis document, whereby a given section presents a group of cards thatare based on the results of a social media search performed inconnection the section. A given section can also comprise logic thatdetermines in what order the cards are ranked and listed.

For instance, the logic for the quick chat section can rank results by:best friends; recent conversations (e.g., media content item or chatinteractions, exclude new friends with no interactions); andbi-directional friendships. In the quick chat section, a user can tap ona card to enter a chat, double-tap on a card to media content item, orpress and hold a card for a mini profile for the user represented by thecard.

The logic for the new friends section can list results by most recentnew friends added by the user (e.g., newest friend at top). This sectioncan include my most recent two-way friends that the user has made withinthe last 30, and if there are fewer than 5 friends made within the last30 days, include up to 5 of the most recent friends made from beyond 30days.

The logic for the quick add section can list people that the user maylikely want to quickly add as friends, and may list the people accordingto a rank (e.g., most likely to least likely).

The logic for the contacts section can list other users that arecorrespond to contacts in a user's address book (e.g., an address booklocal to the user device but external to the social network client onthe user device). Below the other users in the address book, the usermay see people in the user's address book who they can invite to thesocial network platform. If a user does not have any people in his orher address book, he or she may not see the contacts section.

FIGS. 3A and 3B is a screenshot of an example graphical user interfacethat presents a pre-typing view in accordance with some embodimentsdescribed in this document. The pre-typing view includes: a specialoccasion (birthdays, breaking news, etc.) cards section; an emojicarousel section; an enhanced quick chat carousel section; a topicscarousel section; a live stories carousel section; an around me carouselsection; media content item tabs section includes last night,highlights, sports, and pets; a discover carousel section; and user tabssection.

FIGS. 4-12 are screenshots of example graphical user interfaces, each ofwhich presents a section of a pre-typing view in accordance with someembodiments described in this document. In particular, FIG. 4 presentsscreenshots relating to a special occasions section of a pre-typingview, which includes things happening nearby, birthdays, and thingshappening at your current place.

FIG. 5 presents a screenshot relating to an emoji shortcuts section,which comprises of a horizontal scroll of emojis in pills that, afterselection (e.g., user finger tap), take a user into a search experienceas if they had typed the word that is associated with that emoji. Thelisting of emoji pills can be presented as a carousel such that a usercan swipe in one direction and loop back to the opposite end of thelisting. This may be accomplished by an associated word being enteredinto the search box when an emoji is tapped by the user. Examples ofemoji-word association include, without limitation: a tongue with theword “restaurants”; dolphin with the word “dolphin”; a Japanese flagwith the word japan”; a basketball with the word “nba”; statue ofliberty with the word “new york”; a drink with the word “bars”; and atable tennis paddle with the word “table tennis.” Example types of emojishortcuts include, without limitation, locations (e.g., “Japan”, “NewYork”), categories (e.g., “Bars” and “Restaurants”), topics (e.g.,“dolphin” and “table tennis”); and events.

FIG. 6 presents a screenshot relating to a quick chat section, whichlists people that the user may likely want to quickly add as friends.The illustrated user interface permits a user to horizontally scrollthrough the listing of people and permit the user to chat with a listedperson by selecting (e.g., tapping) on a graphic representing the listedperson. The listing can be presented as a carousel such that a user canswipe in one direction and loop back to the opposite end of the listing.

FIG. 7 presents screenshots relating to a topics section, which listssocial media content (e.g., stories) based on a rank, such as a userrelevance (e.g., user's region or past viewing interests). Theillustrated user interface permits a user to horizontally scroll throughthe listing of social media content and permit the user to view or openspecific content by selecting (e.g., tapping) on the graphic (e.g.,card) representing the specific content. The listing can be presented asa carousel such that a user can swipe in one direction and loop back tothe opposite end of the listing.

FIG. 8 presents screenshots relating to a live stories section, whichpresents a personalized list of social media content (e.g., topgeo-based clusters from all over the world) that relating to livestories (e.g., concerts, sports, cultural events current occurring). Theillustrated user interface permits a user to horizontally scroll throughthe listing of social media content and permit the user to permit theuser to view or open specific content by selecting (e.g., tapping) onthe graphic (e.g., card) representing the specific content. The listingcan be presented as a carousel such that a user can swipe in onedirection and loop back to the opposite end of the listing. The itemslisted may be order according to a ranking, which may for example bebased on freshness (e.g., most recent first), quality, and locality ofcontent relative to the user (e.g., list local content first). As alsoshown, the graphics representing each social media content item listedincludes an emoji to represent a classification for the content (e.g.,emoji representing the type of content listed). The emoji presented fora particular listed item may be algorithmically generated or may becurated. If there is no established classifying emoji for the type ofcontent presented by a listed item, a default emoji may be utilized forthe listed item.

FIG. 9 presents screenshots relating to an around me section, whichlists social media content according to its relation to the currentlocation of the user. For instance, the social media content may relateto local fresh events, local popular business establishments, or localonline discussions (e.g., local chatter). The illustrated user interfacepermits a user to horizontally scroll through the listing of socialmedia content and permit the user to permit the user to view or openspecific content by selecting (e.g., tapping) on the graphic (e.g.,card) representing the specific content. The listing can be presented asa carousel such that a user can swipe in one direction and loop back tothe opposite end of the listing. If there are no good listings of socialmedia content (e.g., clusters) relating to the current location of theuser (e.g., their neighborhood), then the section may present a listingof social media content based on a wider geographic region (e.g., city)relative to the user's current location. If there are no good listingsof social media content relating to the wider geographic region (e.g.,city) and an even wider geographic search parameter would surpass agiven threshold (e.g., distance or county), the section may be hiddenentirely. As shown, at the end of listing of social media content, thesection presents a listing of suggestions for search categories, whichwhen selected (e.g., user finger tap) can lead to a full search as ifthe user had typed the word into the search box.

FIGS. 10A and 10B presents screenshots relating to a tab section, whichlists different listings of social media content as tabbed subsections,with each tabbed subsection representing a different social mediacontent category. The example tabbed subsections shown include:neighborhood tab for social media content relating to a neighborhoodassociated or local to the user (e.g., neighborhood content that isrelatively fresh from the last 2-6 hours); a highlights tab for popularor trending social media content (e.g., top stories in a user's regionfrom the past 24 hours or longer if there isn't much content in the past24 hours); a sports tab for sports-related social media content (e.g.,sports events happening in the user's country or globally, with localcontent being prioritized first); a pets tab for social media contentrelating to pets (e.g., dogs or cats); a fashion tab for social mediacontent relating to fashion (e.g., fashion events happening in theuser's country or globally, with local content being prioritized first);an attractions tab for social media relating to current or localattractions; a shows tab for social media content relating to concerts(e.g., concerts happening in the user's country or globally, with localcontent being prioritized first); a breaking now tab for the most recentsocial media content, and a concert tab for concert-related social mediacontent. The illustrated user interface permits a user to horizontallyscroll through the listing of tabbed subsections and permit the user topermit the user to view a specific tabbed subsection by selecting (e.g.,tapping) on the label representing the specific tabbed subsection. Thelisting can be presented as a carousel such that a user can swipe in onedirection and loop back to the opposite end of the listing. The listingof social media content for a given tabbed subsection is presentedvertically, thereby permitting the user to vertically scroll through thelisting and select a listed social media content item.

The set of tabbed subsections presented to the user may be static,dynamic, or a combination of both. For instance, the tabbed subsectionsmay be dynamic such that a user does not always see the same ordering ordefault tabbed subsections when they horizontally scroll through thetabs. The ordering may change depending on context, so for example if itis the weekend, a “Parties” tabbed subsection may be presented andinclude social media content relating to upcoming, on-going, or recentparties. The neighborhood tab may be presented if a user is in a metroarea that is large enough to have neighborhoods (e.g., Los Angeles orNew York). The attractions tab may be presented if a user is in a regionthat is large enough to have local attractions.

FIG. 11 presents screenshots relating to a discover section, which listsof social media content that is featured, or sponsored/provided by athird-party (e.g., blog, news agency, publisher, company, etc.). Theillustrated user interface permits a user to horizontally scroll throughthe listing of social media content and permit the user to permit theuser to view or open specific content by selecting (e.g., tapping) onthe graphic (e.g., card) representing the specific content. The listingcan be presented as a carousel such that a user can swipe in onedirection and loop back to the opposite end of the listing. The itemslisted may be order according to a ranking, which may for example bebased on freshness (e.g., most recent first), quality, and locality ofcontent relative to the user (e.g., list local content first).

FIG. 12 presents screenshots relating to a friend tab section, whichlists different listings of social network users as tabbed subsections.The example tabbed subsections shown include: new friends; quick add;and my contacts. Each of these tabbed subsections can be respectivelysimilar to the new friends, quick add, and contacts sections describedabove with respect to FIG. 2. The illustrated user interface permits auser to horizontally scroll through the listing of tabbed subsectionsand permit the user to permit the user to view a specific tabbedsubsection by selecting (e.g., tapping) on the label representing thespecific tabbed subsection. The listing can be presented as a carouselsuch that a user can swipe in one direction and loop back to theopposite end of the listing.

FIG. 13 is a screenshot of an example graphical user interface thatpresents a post-typing view in accordance with some embodimentsdescribed in this document. As noted in this document, the post-typingview appears on the user interface in the presence of a user parameterfor executing a social media search. For instance, the post-typing viewappears as soon as the user begins to enter characters of a search query(e.g., keyword or search string) into the search box. For a given searchquery provided by the user, the hero card presents the user with theresults for the given search query, which includes a listing of socialmedia content that best matches the given search query. Freshness of thesocial media content can be used to filter social media content includedin the listing, and can be used to order the social media content. Themore stories section presents the user with a listing of social mediacontent (e.g., stories) that is directly or indirectly related to theresults of the search query, which the section can present according totopic, date and time, events, or co-occurrence. For instance, withrespect to indirectly related social media content, the more storiessection can present the user with best results from at least one relatedsearch (e.g., performed by the user or another user). The related peoplesection presents the user with a listing of other users that are similaror related to the results of the search query. The related searchessection presents the user with the results of similar searches, and caninclude rich content and additional more stories sections.

FIG. 14 are screenshots of an example graphical user interface thatpresent a post-typing view in accordance with some embodiments describedin this document. In particular, FIG. 14 illustrates an example of howthe results of a search query presented to the user are dynamicallyupdated as a user types in more characters into the search box. In FIG.14, the user interface can breaks/separates out the best results as theuser updates the search query and as the updated search query results ina higher confidence level in the search (illustrated in FIG. 14 by theinflection point). In this way, as the user focuses the search query byadding more characters to the search string, the search function canachieve disambiguation.

Starting from the left screenshot in FIG. 14, the user has typed in thesearch string of “abc” and top results for that search query arepresented to the user. As the user adds more characters (“de”) to thesearch string (see middle screenshot), a first subset of top results isbroken out/separated from original set of results based on the mostrecent update to the search string (e.g., more specific search string).As the user further adds more characters (“fg”) to the search string(see right screenshot), a second subset of top results is brokenout/separated from the first subset of results based on the most recentupdate to the search string. As shown, when a subset of top results ispresented to the user, the remaining subset of the results may also bepresented as a separate grouping (e.g., subset of top results presentedabove and separate from the remaining subset).

FIG. 15 are screenshots of an example graphical user interface thatpresents a post-typing view in accordance with some embodimentsdescribed in this document. Similar to FIG. 14, FIG. 15 illustrates anexample of how the results of a search query presented to the user aredynamically updated as a user types in more characters into the searchbox. Starting from the left screenshot in FIG. 15, if a user types onecharacter (“k”), the user is presented with a set of social mediacontent (e.g., social media stories) best matching the search query(“k”). After typing additional characters (“an”) to the search string(see middle screenshot), the user is presented with a first subset oftop results that is broken out/separated from original set of resultsbased on the updated search string (“kan”). As the user further addsmore characters (“ye”) to the search string (see right screenshot), asecond subset of top results is broken out/separated from the firstsubset of results based on the updated search string (“kanye”).

FIG. 16 are screenshots of example graphical user interfaces that eachpresent a view of post-typing view in accordance with some embodimentsdescribed in this document. In particular, screenshot 1602 illustrates amy friends card, screenshot 1604 illustrates group cards, screenshot1606 illustrates add a friend cards, and screenshot 1608 illustrates apublisher card.

For various embodiments, a successful search refers to a situation wherethe search results meet a confidence threshold that permits apost-typing view to present a vertical (a vertical section presentingsearch details) that in addition to just a results section (e.g., herocard section), includes/is fully expanded to show multiple sections thatpresent social media content related to the social media contentprovided in the results section (e.g., a more stories section and arelated search section that directly tie to the results section).Example verticals that can appear in a post-typing view are described inthis document with respect to FIGS. 17 through 25.

FIG. 17 are screenshots of an example graphical user interface thatpresents a specific place vertical of a post-typing view based on asearch query in accordance with some embodiments described in thisdocument. In particular, the specific place vertical presents thefreshest social media content relating to a particular locationidentified by the search query provided. As shown in FIG. 17, at the topof the post-typing view, social media content relating to a specificlocation (“The Bungalow”) identified by the search query (“bungalow”) islisted within a hero card. The hero card indicates whether the socialmedia content has had high volume recently (e.g., within the last 3hours), and provides the location name, the location type, the locationdistance relative to the user's current location, and locationdescriptions. When a user taps on the hero card, the social mediacontent listed in the hero card will play for the user. Each differentspecific place found based on the search query may be presented in aseparate hero card, and the social media content listed within the herocard may play in a chronological order when the hero card is selected bythe user. For instance, when a user selects a hero card, all the socialmedia content listed in the hero card that relate to the most recent day(e.g., today) are played first in chronological order and, subsequently,social media content relating to previous days may be played in achronological order.

For some embodiments, if a user taps on the hero card during a weekend(e.g., night), the content would be fresh and show what it is like atthe specific location (e.g., at “The Bungalow”) right now.Alternatively, if the user taps on the hero card on a Monday, the herocard will show the user the best social media content relating to thespecific location from the weekend. In some instances, the eventsvertical can list social media content from an entire duration of anevent, while if a specific location (e.g., bar or club) has an amount ofrecent social media content (e.g., social media content from the lastfew hours) that exceeds a certain threshold, the user may be presentedwith social media content exclusively from a recent period (e.g., onlythe last few hours).

As shown in FIG. 17, the specific location vertical includes a morestories section and a related searches section under the hero card thatlists the results of the search query. The more stories section presentssocial media content (e.g., stories) that are directly or indirectlyrelated to the set of specific locations presented by hero cards. Forinstance, the more stories section presents clusters of social mediacontent that tie directly to the geographic location of the specificlocation, or social media content relating to concepts that arespecifically tied to the specific location (e.g., social media contentfrom over the past week that was created at “The Bungalow” and relatesspecific to the specific concept of “Pool Table” and “Dance Floor”). Themore stories section can also present social media content on moregeneral topics or social media content similar (or nearby) to thespecific location (e.g., “The Room”). The related searches sectionpresents social media content identified by a search related to thecurrent search (e.g., one having a similar search query). The relatedsearches can teach users that they can search using broader searchqueries (e.g., broad locations like neighborhoods, or topics that willbe associated with locations like “Cocktails”).

FIG. 18 are screenshots of an example graphical user interface thatpresents a person vertical of a post-typing view based on a search queryin accordance with some embodiments described in this document. Inparticular, the person vertical presents the freshest social mediacontent relating to a particular person identified by the search queryprovided. As shown in FIG. 18, at the top of the post-typing view,social media content relating to a person (“Kanye West”) identified bythe search query (“kanye”) is listed within a hero card. The hero cardprovides the person's full name or stage name, a description of theperson (e.g., rapper), and a caption representative of the person (e.g.,one sampled from social media content relating to the person and whichmay include an emoji). When a user taps on the hero card, the socialmedia content listed in the hero card will play for the user. Eachdifferent person found based on the search query may be presented in aseparate hero card, and the social media content listed within the herocard may play in a chronological order (e.g., reverse chronologicalorder) when the hero card is selected by the user. For instance, when auser selects a hero card, all the social media content listed in thehero card that relate to the most recent day (e.g., today) are playedfirst in chronological order and, subsequently, social media contentrelating to previous days may be played in a chronological order.

As shown in FIG. 18, the person vertical includes a more storiessection, a related people section, and a related searches section underthe hero card that lists the results of the search query. The morestories section presents social media content (e.g., stories) that aredirectly or indirectly related to the set of persons presented by herocards. For instance, the more stories section presents clusters ofsocial media content that tie directly to the particular person, orsocial media content relating to concepts (e.g., events, fashion, music)that are specifically tied to the person. The section may present, forexample, social media content relating to events such as “KanyeCancelled,” the prevailing title given to a Kanye West concert inSacramento where he showed up for 15 minutes and then cancelled. Asanother example, the section can present social media content relatingto a stories that take place over a longer period of time, such as“Kanye 2020” which relates to Kanye running for election in 2020. Therelated person section presents other people related to the personspresented by the hero cards (e.g., “Kim Kardashian,” Kanye's West'swife). The related searches section presents social media contentidentified by a search related to the current search (e.g., searches forother famous people like “kanye,” or search for hip hop music relatedsocial media content as a result of “kanye” being a hip hop musician).

FIG. 19 are screenshots of an example graphical user interface thatpresents a topic vertical of a post-typing view based on a search queryin accordance with some embodiments described in this document. Inparticular, the topic vertical presents the freshest social mediacontent relating to a particular topic identified by the search queryprovided. As shown in FIG. 19, at the top of the post-typing view,social media content relating to a topic (“yoga”) identified by thesearch query (“yoga”) is listed within a hero card. The hero cardindicates whether the social media content has had high volume recently(e.g., within the last 3 hours), and provides the topic name, and acaption representative of the topic (e.g., one sampled from social mediacontent relating to the topic and which may include an emoji). When auser taps on the hero card, the social media content listed in the herocard will play for the user. Each different specific topic found basedon the search query may be presented in a separate hero card, and thesocial media content listed within the hero card may play in achronological order (e.g., reverse chronological order) when the herocard is selected by the user.

As shown in FIG. 19, the topic vertical includes a more stories section,a related people section, and a related searches section under the herocard that lists the results of the search query. The more storiessection presents social media content (e.g., stories) that are directlyor indirectly related to the set of topics presented by hero cards. Forinstance, the more stories section presents clusters of social mediacontent that tie directly to concepts (e.g., flexible, meditation,stretch, juice, workout, and gym) that are specifically tied to thetopic (“yoga”). The related person section presents other people relatedto the topics presented by the hero cards. The other people may bepresented by rank order according to, for example, their relation to thetopic presented by the hero cards or by account activity of those otherpeople (e.g., prioritize accounts that currently have an active storyand one that is long). The related searches section presents socialmedia content identified by a search related to the current search(e.g., one having a similar search query).

FIG. 20 is a screenshot of an example graphical user interface thatpresents a topic vertical of a post-typing view (based on a searchquery) that includes a discover section in accordance with someembodiments described in this document. As shown in FIG. 20, at the topof the post-typing view, social media content relating to a topic(“Manniquin Challenge”) identified by the search query (“manneq”) islisted within a hero card. Based on the results presented in the herocard, the discover section of the vertical presents a social mediacontent relating to the “20 Mannequin Challenge videos you can't miss,”which is provided by a third-party.

FIG. 21 is a screenshot of an example graphical user interface thatpresents a vertical in a post-typing view based on a search queryincluding an emoji in accordance with some embodiments described in thisdocument. In particular, the vertical presents the freshest social mediacontent identified by the search query that includes an emoji. As shownin FIG. 21, at the top of the post-typing view, social media contentidentified by the search query (“heart emoji”) is listed within a herocard. The vertical includes a more stories section and a relatedsearches section under the hero card that lists the results of thesearch query. The more stories section presents social media content(e.g., stories) that are directly or indirectly related to the resultsto the set of specific locations presented by hero cards. For instance,the more stories section presents clusters of social media content thathave titles or descriptions that also include the same emoji. Therelated searches section presents social media content identified by asearch related to the current search, which in this case can includeemoji similar or related to an emoji included in the search query (e.g.,heart-break emoji).

FIG. 22 are screenshots of an example graphical user interface thatpresents a broad location vertical of a post-typing view based on asearch query in accordance with some embodiments described in thisdocument. In particular, the broad place vertical presents the freshestsocial media content relating to a broach place identified by the searchquery provided, such as a city (e.g., New York or Santa Monica). Asshown in FIG. 22, at the top of the post-typing view, social mediacontent relating to a broad place (“New York”) identified by the searchquery (“nyc”) is listed within a hero card. The hero card provides thebroad location name, city, state, and county as applicable, and acaption representative of the broad place (e.g., one sampled from socialmedia content relating to the topic and which may include an emoji).When a user taps on the hero card, the social media content listed inthe hero card will play for the user. Each different specific broadplace found based on the search query may be presented in a separatehero card, and the social media content listed within the hero card mayplay in a chronological order when the hero card is selected by theuser.

As shown in FIG. 22, the topic vertical includes a live section and arelated searches section under the hero card that lists the results ofthe search query. The live section presents social media content (e.g.,stories) that currently occurring or recently occurred directly orindirectly in relation to the set of broad places presented by herocards. The related searches section presents social media contentidentified by a search related to the current search (e.g., one having asimilar search query), such as Brooklyn, Manhattan, Los Angeles, andChicago.

FIG. 23 are screenshots of an example graphical user interface thatpresents an events vertical of a post-typing view based on a searchquery in accordance with some embodiments described in this document. Inparticular, the events vertical presents the freshest social mediacontent relating to a particular event identified by the search queryprovided. As shown in FIG. 23, at the top of the post-typing view,social media content relating to a topic (“Formation Tour”) identifiedby the search query (“formation tour”) is listed within a hero card. Thehero card indicates whether the social media content has had high volumerecently (e.g., within the last 3 hours), and provides the event name,and a caption representative of the event (e.g., one sampled from socialmedia content relating to the topic and which may include an emoji).When a user taps on the hero card, the social media content listed inthe hero card will play for the user. Each different specific topicfound based on the search query may be presented in a separate herocard, and the social media content listed within the hero card may playin a chronological order (e.g., reverse chronological order) when thehero card is selected by the user.

As shown in FIG. 23, the topic vertical includes a more stories section,a related people section, and a related searches section under the herocard that lists the results of the search query. The more storiessection presents social media content (e.g., stories) that are directlyor indirectly related to the set of topics presented by hero cards. Forinstance, the more stories section presents clusters of social mediacontent that tie directly to individuals or events (e.g., Beyonce, JayZ, and Kanye Cancelled) that are specifically tied to the topic(“Formation Tour”). Under the related person section, if an event has aset of main related people who also have an account, that accountappears as an add friend card under the related person section. Therelated searches section presents social media content identified by asearch related to the current search (e.g., one having a similar searchquery).

FIGS. 24 and 25 are screenshots of example graphical user interfacesthat each present a category vertical of a post-typing view based on asearch query in accordance with some embodiments described in thisdocument. In particular, both FIGS. 24 and 25 illustrate categoriesverticals that present the freshest social media content relating to aparticular category identified by the search query provided. FIG. 24presents a categories vertical without a more stories section, and FIG.25 presents a categories vertical with a more stories section.

As shown in FIG. 24, at the top of the post-typing view, social mediacontent relating to a category (“Bars”) identified by the search query(“bar”) is listed within a hero card. The hero card provides a categoryname, city, state, and county as applicable, and a captionrepresentative of the category (e.g., one sampled from social mediacontent relating to the topic and which may include an emoji). When auser taps on the hero card, the social media content listed in the herocard will play for the user. Each different specific topic found basedon the search query may be presented in a separate hero card, and thesocial media content listed within the hero card may play in achronological order (e.g., reverse chronological order) when the herocard is selected by the user. As also shown in FIG. 24, the categoriesvertical includes a special list section, in this case a near me sectionthat provides a listing of bars that are near the user's currentlocation.

For FIG. 25, at the top of the post-typing view, social media contentrelating to a category (“Burrito”) identified by the search query(“burrito”) is listed within a hero card. The categories verticalincludes a listing of bars under the “near me” section to present barsthat are near the user's current location and, additionally includes amore stories section, which presents social media content (e.g.,stories) that are directly or indirectly related to the set ofcategories presented by hero cards.

FIGS. 26A and 26B are screenshots of example graphical user interfacesthat present a post-typing view under different search scenarios inaccordance with some embodiments described in this document. Under a noor poor results search scenario, some embodiments broaden results whenno results at a specific place are found. When there is no a typo andthe search query is not a place, query suggestions can be provided to auser. Poor media content item results may comprise less than 5 mediacontent items for a search.

Screenshot 2602 illustrates where there are no results for exact queryand only one result is presented. Screenshot 2604 illustrates wherethere are no results for exact query and multiple results are presented.Screenshot 2606 illustrates where there are no results for exact queryand there is confidence that the search query contains a typo.Screenshot 2608 illustrates where a user is searching for a concept(“ink”) and result contain the concept sought and a specific place alsohaving the same name as the concept. Screenshot 2610 illustrates where auser is searching for a specific place or broad location (“nopa”) andthe result are provided. Screenshot 2612 illustrates where a user issearching for a specific topic, person, band or event (“spoon”) and themix results are provided. Screenshot 2614 illustrates where a user issearching for a specific place (“eggslut”) and the result indicate thereare multiple locations near the user. Screenshot 2616 illustrates wherea user is searching for a topic story (“mcdonalds”) and the resultsprovide a topic story and multiple specific locations.

FIGS. 27A-27D are screenshots of example graphical user interfaces thatpresent a post-typing view based on client-side caching in accordancewith some embodiments described in this document. Screenshot 2702illustrates a graphical user interface when no content has been cachedat the client and the graphical user interface is initially loading.Screenshot 2704 illustrates a graphical user interface when content hasbeen cached at the client.

FIG. 28 are screenshots of example graphical user interfaces thatpresent different states of a post-typing view in accordance with someembodiments described in this document. In particular, the leftmostscreenshot illustrates the state of the post-typing view when a user hasbegun to enter a search query. The middle screen illustrates the stateof the post-typing view when a user has selected to view an item fullscreen. The rightmost screenshot illustrates the state of thepost-typing view when a user has exited or completed a story.

FIG. 29 is a diagram illustrating an example indexing infrastructure inaccordance with some embodiments described in this document. Inparticular, the illustrated indexing infrastructure comprises (1) anindexing pipeline that creates dynamic stories from social media content(e.g., submit-to-live media content item content), and (2) a servinginfrastructure that surfaces these dynamic stories to users through thesearch bar. Though FIG. 29 is described with respect to media contentitems, it will be understood that some embodiments can be utilized withother types of social media content.

In FIG. 29, the indexing pipeline comprises of two stages: Annotations;and Dynamic Story Creation. Joiner of the Annotations stage receivesMetadata updates from FSN when a new media content item (e.g., snap) isposted to a live submission feed (e.g., “Our Story”). The new mediacontent item is passed through a set of annotators (Annotation Services)that add knowledge (“annotations”) about the new media content item andstore the annotations and metadata of the new media content item in adata structure, represented in FIG. 29 as a protocol buffer (proto)called a Join. As used in this document, a proto can comprise alanguage-neutral, platform-neutral mechanism for serializing data. TheJoin is written to a database, which represented in FIG. 29 by JoinsBigtable. For some embodiments, Joins Bigtable is implemented by aGOOGLE NoSQL Big Data database service. Additionally, for someembodiments, each of the Annotation Services and the Joiner isimplemented by a GOOGLE Container Engine based on KUBERNETES (GKE).

Flow of the Dynamic Story Creation stage obtains a set of Joins fromJoins Bigtable. Flow comprises a set of data flow pipelines each ofwhich takes in Joins from over a period of time (e.g., past 24 hours).Based on the Joins and side data models, Flow generates dynamic stories.For some embodiments, Flow runs clustering algorithms (ClusterersService) over different dimensions, such as geolocation, time, similarcaption terms, and the like. The clustering results from ClusterersService are stored in a database, which is represented in FIG. 29 byCluster Bigtable. For some embodiments, Cluster Bigtable (BT) isimplemented by a GOOGLE NoSQL Big Data database service. Clusteringresults are also stored to an index of a search service (ElasticSearch)for serving clustering results to users as search results. ElasticSearchmay be implemented by an open-source search technology, such as APACHELucene™.

For some embodiments, Clusterers Service comprises a service thatexposes multiple remote procedure calls (e.g., gRPC) for differentclustering tasks. As shown, Clusterers Service is implemented as aservice running in GKE. Clusterers Service may be implemented as aPython service. Additionally, Clusterers Service may operate based onstored models (e.g., GCS), utilize API calls to Place Server to computeplace names for generated stories, and datastore calls for curation datarelated to clusters and stories.

For some embodiments, ElasticSearch comprises an index for servingstories, and may store one index per a Flow type (e.g., Topic, Event,and Places Flow as described in this document). For a given Flow, theindex may be swapped at each run, resulting in all dynamic storiesgenerated on previous runs of the given Flow being removed and replacedwith dynamic stories generated from the latest run of the given Flow.For some streaming and incremental Flows (such as Places Flow), theindex may not be swapped and a single long-running index is maintainedwhere dynamic stories are added and removed over time.

Various embodiments may maintain a separate ElasticSearch cluster forindexing where the separate ElasticSearch cluster stores Cluster outputs(e.g., rather than dynamic stories) produced by Flows. Such a separateElasticSearch cluster may be utilized to store a period of Joins (e.g.,past 24 hours) to provide search and analysis functionality for indexingpipelines, curation workflows, debug tasks, or some combination thereof.

For some examples, the architecture of ElasticSearch comprises a clusterdeployed as a GKE cluster with dedicated node pools for data nodes,client nodes, and master nodes. Client nodes may expose a NodePortService for the HTTP endpoint and the ElasticSearch Transport endpoint.To dynamically discover the virtual machines that participate in theNodePort Service, GOOGLE Internal LoadBalancer may be utilized, whichprovides an internal frontend IP address and port forwards to the IPaddresses of the cluster.

FIG. 30 is a diagram illustrating an example indexing infrastructure inaccordance with some embodiments described in this document. Inparticular, FIG. 30 illustrates the Annotations stage in detail. Asshown, Joiner comprises Joiner, which is the entry point to the indexingpipeline. Joiner may comprise a GO server running on GKE. Joiner listensto media content item metadata updates (e.g., from FSN via GOOGLE CloudPubSub). A media content item metadata update may be sent whenever amedia content item is posted to live submission feed or when there is amedia content item deletion notification. To perform media content itemannotations based on the media content item content, Joiner fetches themedia content item's media via an HTTP call (media fetch) to the FSNendpoint. This fetch call may be authenticated using a shared secretthat is stored in encrypted form in a datastore. Joiner then runsmultiple annotator plugins that derive extra knowledge about the mediacontent item and puts it all together in a Join proto. For someembodiments, the Join is written to two databases: Join Bigtable whichcan store all Joins created in the past 3 months; and a secondary indexJoin Bigtable—24 h, which can store all Joins created in the last 24hours.

As also shown, Annotator comprises Media Processor (MP), which runsmultiple machine intelligence data flow graph models (e.g., TensorFlow™models) on media content item content to compute labels (e.g. stadium,concert, basketball) and metrics (e.g. media quality, shakiness score).The data flow models may be trained offline and loaded at startup time.SMP may also compute the best thumbnail frame for a media content itemincluding video media. SMP may be implemented as a C++ service operatingon GKE. SMP exposes a remote procedure call (RPC) interface, which mayonly be available to internal services of the indexing infrastructure.

A user features of Annotator can run as a plugin inside of Joiner. Thisuser features annotator reads user statistics from a datastore anddenormalizes them into a Join. The statistics are computed by a separatepipeline by reading data from FSN and SC-Analytics and storing them on adatastore in the indexing infrastructure.

FIG. 31 is a diagram illustrating an example indexing infrastructure inaccordance with some embodiments described in this document. Inparticular, FIG. 31 illustrates the Place portion of Annotator indetail. Place Annotator represents the Place portion of Annotator isrunning inside of Joiner (e.g., as a plugin). Place Annotator performs areverse geocoding call to Place Server to find the most likely location(e.g., place name, city, etc.) of the user based on thelatitude/longitude of the media content item. Place Server may beimplemented as a simple GKE service that uses GO and works as a cachelayer on top of a Maps Geocoding API. Place Server may expose itself asan internal only HTTP API.

FIG. 32 is a diagram illustrating an example indexing infrastructure inaccordance with some embodiments described in this document. Inparticular, FIG. 32 illustrates the Dynamic Story Creation stage indetail for Topics Flows.

As noted in this document, Flow supports creation of dynamic storiesfrom a set of Joins and implemented as a dataflow pipeline. For someembodiments, Flow scans a given time window of Join updates (e.g., 1day, 30 minutes, etc.). Flow bins the Joins based on a set of selectioncriteria (e.g., media content items for the same geo region). Flowperforms an external remote procedure call (RPC) request Clustererservice, which returns clusters computed (by the clustering algorithm)from binned Joins. Flow then writes the output (clustering results) toCluster Bigtable and to an index of a search service, ElasticSearch.

FIG. 32 illustrates a Flow particularly implemented for topics andcreates dynamic stories based on distinctive terms (e.g., eithercaptions or labels). According to some embodiments, cron services isconfigured to send a request to an internal endpoint serviced by FlowScheduler. The endpoint may be authenticated by a standardauthentication (e.g., GOOGLE authentication) and may require both avalid admin account. Flow Scheduler invokes a Topics Flow to run onDataflow (e.g., GOOGLE Dataflow). The pipeline of the Topics Flowreceives as input: Joins over specific time period (e.g., 24 hours ofJoins); a set of side models from GCS; and Clusters generated by aprevious run. The Topics Flow then performs two separate remoteprocedures (RPCs) that are made to clusterers of Topics Clusterer, whichis part of Clusterers Service. The first RPCs are performed per a topicclustering (e.g., all media content items with the term ‘love’, ‘MLK’,etc.), which creates “singleton” clusters. This topic clustering mayscore, state, and then build the “singleton” clusters. After the“singleton” clusters are created, the second RPCs are called with a setof top trending clusterers (e.g., top 400) to create stories about thetrending topics. This second clusterer call may involve merging the toptrending clusters.

The output of the Clusterers Service is a set of Cluster protos, whichcan contain all information about the clusters that were created byTopics Clusterer. The set of Cluster protos are stored on ClusterBigtable. For serving Topic dynamic stories to search clients inElasticSearch, the set of Cluster may be transformed into a set ofdynamic story (DynamicStory) protos, which contain the necessary fieldsfor serving stories of the set of Cluster. This set of DynamicStoryprotos may be written to the index of the ElasticSearch.

Flows to create other types of stories (other than topic stories) may beimplemented similar to Topics Flow. For example, Events Flow createsevent stories based on geographical and time proximity. Events Flow mayhave a flow similar to Topics Flow with the modification of binningJoins into regions and performing single clustering calls per a region.

In another example, MetaStories Flow creates stories from other stories.MetaStories Flow may have a flow similar to Topics Flow with themodification of that the input is no longer Joins, but rather Clusters,and Clusterer Service is called for metastories.

FIG. 33 is a diagram illustrating an example indexing infrastructure inaccordance with some embodiments described in this document. Inparticular, FIG. 33 illustrates the Dynamic Story Creation stage indetail for Places Flows. For some embodiments, a selection stage worksas an annotator for Joins, and story generation happens in a streamingdataflow as media content items are updated. In FIG. 33, Places Flowcomprises two separate Flows: ItemToPlace Flow and PlaceStories Flow.ItemToPlace Flow bins Joins for a period of time (e.g., past 15minutes). The Joins may be binned by S2 cells at level 16, which uses anindex S2-to-Place Bigtable to bin the Joins. The Joins are joined withall the known Places (e.g., provided to S2 Cell). Subsequently, a callto Clusterer Service is performed (by Item-To-Place) to assign mediacontent items to known Places. The known Places about may be stored inPlace Bigtable. After media content items have been assigned knownPlaces, the Place Ids with newly assigned Joins are sent (via GOOGLECloud PubSub) to PlaceStories Flow, a streaming Dataflow job performsstory creation. ItemToPlace Flow writes back the Place assignment to theJoin Bigtable as an annotation. This Place assignment may be moreprecise than the original Place annotation done by Joiner. ItemToPlaceFlow also writes the Join ids that were matched to a place to the PlaceBigtable, which creates a Place Id→Join Ids index.

PlaceStories Flow receives (via GOOGLE Cloud PubSub) a message whichtriggers the dynamic story creation for a given Place. First the PlaceBigtable is queried to retrieve all Join Ids that have been assigned tothat Place, and then the actual Joins are retrieved from the JoinsBigtable. Clusterer Service is called to form Clusters for the Place andresults are stored as described with respect to Topic Flow.

FIG. 34 is a diagram illustrating an example serving infrastructure inaccordance with some embodiments described in this document. The servinginfrastructure of FIG. 34 comprises a search stack and an auxiliarysuggest stack. The search stack is the main endpoint that takes in auser query and the user's location and generates search results with alldynamic stories that best match the user's interest. The suggest stack(represented in part by Suggest Service) takes in a partial user queryand, as the user is typing, returns a list of suggestions for completionof the user query. For some embodiments, Suggest Service runs an inmemory prefix search (e.g., in-memory APACHE Lucene™ index) over dynamicstories generated to suggest the completions for a user query. SuggestService may be a Java-based service running on Flex Appengine.

For some embodiments, a Suggest Request is triggered every X charactersor second, and a client (e.g., mobile client) makes a request to theQuery Suggest endpoint in the FSN. The FSN may perform clientauthentication and passes the request through to Suggest Service byissuing a URL fetch request. Suggest Service does an in-memory searchusing the user query against an APACHE Lucene™ index created fromsuggestions generated by a Flow pipeline (e.g., Topics Flow). The topset of query suggestions are returned to the FSN and passed through tothe client to show the user. Suggest Service may operate as a separateservice with its own in-memory APACHE Lucene™ index to avoid latencysensitive issues. Also, the Lucene™ index may be regeneratedperiodically by reading the latest output from a Flow pipelines.

With respect to the search stack, a user queries from a client (e.g.,mobile device client) are sent to the search stack (via the FSN as aSearch Request). The Search Request is passed through to RankingService, which may be a Python HTTP server running on GAE Flex. RankingService may run multiple producers based on the user's query andlocation to create the different sections for the user. Each product(e.g., EmojiCarrousel) issues queries against the indices ofElasticSearch for the dynamic stories that were generated by theindexing pipeline (e.g., Flow). Dynamic stories returned by theElasticSearch are ranked, deduped across producers, and then combinedinto a final Search Response and sent back to the client (via the FSN).The FSN takes the Search Response from Ranking Service and passes itthrough to the client.

For various embodiments, search features described in this documentutilize session logging. For instance, for a given query event, thefollowing fields could be logged: language preference, which provides acomma-separated list of a user's device-level language preferences;query_is_suggestion, which indicates whether a query comes from a searchsuggestion; and source, which identifies the page source of a searchsession. In another instance, for result sections that return dynamicstories, events are be logged by a search backend. In yet anotherinstance, a fields keep track user actions with respect to dynamicstories (e.g., skip to next, go to previous, exit story view, etc.), andtrack physical gestures of user action (e.g., swipe, tap, tap & hold,etc.) when viewing a dynamic story.

FIGS. 35 and 36 are screenshots of example graphical user interfaces fora social media content curation tool in accordance with some embodimentsdescribed in this document. In particular, FIGS. 35 and 36 illustrategraphical user interfaces for a curation tool that is used to collect,gather, and create metadata and associate it to dynamic stories builtfrom media content items submitted. The graphical user interfacepresents dynamic stories to curators, and curators can add, remove orcorrect elements of the dynamic stories (e.g., title, place, event name,etc.). These changes may be regarded as story metadata which is storedand fed back into a ranking service to improve algorithms and to ensurethat similar dynamic stories apply the changes. Access to the curationtool and accompanying system may be restricted to an explicit whitelistto control access.

As used in this document, dynamic story can comprise a grouping of storymedia content items based on a topic (e.g., shares an event or a placeor other theme). A curator can review a dynamic story (e.g., fetchedfrom Ranking Service) and decide whether to add/remove/make a change tothe story. Curated Data comprises corrections and other changes producedby a curator after she/he reviews a dynamic story within the curationtool. A context card comprises a small card user interface that shows aname, an image, and a description.

For some embodiments, the curation tool comprises a web frontendcomponent, a data storage system, a media access component, and a dataenrichment system. The web frontend provides a curator (e.g., via a webbrowser) with a graphical user interfaces (e.g., FIGS. 35 and 36) thatpresents a dynamic story and its associated elements (e.g., caption,location, event name, event type, etc.) for review. The data storagesystem stores corrections from curators and, as such, each element ofstored curated data may include a curator's id, time, and dynamic storyid along with the corrected elements. The media access component enablesaccess to social media content via the FSN described in this document.The data enrichment system uses data from external sources (e.g.,Wikipedia) to augment what is known about a dynamic story. For instance,after it known that a specific dynamic story a presidential rally, acurator should be able to associate the dynamic story with thepresidential candidate and all information associated with thepresidential candidate (e.g., full name, age, political affiliation,etc.).

Components of curation data can include, without limitation: caption ortitle of a dynamic story; place name associated with the story; entitiesassociated with the story (e.g., performer for a concert story, or thepolitician for a rally story); information about the associated entities(e.g., name, user identifier, official website link, associatedWikipedia page link; link to a picture of the entity (e.g., posted onWikipedia page). Components of curated data can also include, withoutlimitation: Boolean indicating whether a story contains media contentitems from multiple locations/events (e.g., a mixed story); Booleanindicating whether a story has captions predominantly from a non-Englishlanguage; Boolean indicating whether a story being offensive; storyrating for how interesting the story is (e.g., discrete values such as{awful, bad, ok, good, love it}); emoji character(s) associated with thestory (e.g., curator choose which emoji is best associated with astory); cover media content item id (e.g., curators can change the covermedia content item if they feel the cover media content item could bebetter); removed media content item ids (for media content items removedfrom the story because, for instance, they are low quality (filled withunappealing media content items that do not form a clear story),offensive or blurry etc.); highlight media content item ids (e.g., up to10 media content item ids that represent the highlights of the story);and keywords associated with a story (e.g., curator provide a fewkeywords that are relevant to the story—based on media content items andnot restricted to captions); Boolean indicating relevance of sub storiesto main story; newsworthiness (e.g., (not news, local, city, state,country, global)); and related news URLs.

To facilitate certain features, the curation tool utilizes APIs (e.g.,GOOGLE APIs), for obtaining location information, knowledge graphs, andperforming web searches. Such APIs can enable curation tool to present,for example, locations, related entities, and news related to entitiesin a dynamic story. The curation tool can also utilize data extractedfrom public sources to find attributes of an entity (e.g., like stadiumcapacity), which might be useful to present in a context card.

For various embodiments, curated data generated by the curation tool ismaintained separate from user data, no media is stored with the curateddata, and no curated is copied or stored by the curation tool. Throughthe curation tool, curated data can be used to generate context cards,apply changes provided by curators to other similar dynamic stories, andcollect store changes to dynamic stores from curators. In this way,curators can implement corrections and add enrichment (e.g., add title,place name, selection of representative media content items, links tonews, etc.) to a dynamic story. Curation may be scheduled to take placeafter media content items are selected algorithmically to createstories.

With respect to a dynamic story, information of interest for curationmay include, without limitation, an associated place, associated people,associated organizations, associated events, and the like. Dynamic storymetadata can include, without limitation: caption or title correction,where captions may be indirectly derived from user captions on mediacontent items; dynamic story category (e.g.,concerts/games/fashion/food/politics); place name correction, whereplace name may be derived from GPS info on user media content items andGoogle Places API; place bounding box correction (e.g., outline theboundary of the place on the map); entity metadata, which is informationabout a specific entity (e.g., Kendrick Lamar) and may be from publicsources (e.g., like Wikipedia); place metadata, which is informationabout a specific entity (e.g., Staples Center) and may be from publicsources public sources; entity-story association, which can tie anentity (e.g., Kendrick Lamar) to a specific dynamic story (e.g., he/sheis performing there, or is in the audience, or is a producer of the showetc.); curator information (e.g., name); and time curation wassubmitted.

With respect to some embodiments, curating dynamic stories by thecuration tool involves: the title of the story; place name; bounding boxfor the place; whether it is a mixed story; whether the story is innon-English; whether the story contains offensive material; whether thestory is low quality; emoji characters associated with the story;ratings for how interesting the story is; or context cards forplaces/people/organizations/events related to the story.

For various embodiments, the curation tool is utilized to curate conceptstories (“chatter” stories) around a concept or topic rather than anevent that happens at a specific time or place. For instance, a conceptstory for “Snow” could show media content items from all over the UnitedStates on dramatic/interesting snow maps. Curation with the tool mayinvolve: the title of the story; whether the story is in non-English;whether the story contains offensive material; emoji charactersassociated with the story; ratings for how interesting the story is;remove low quality media content items; pick cover media content item;pick highlight media content items; keywords for the story; or contextcards for places/people/organizations/events related to the story.

For some embodiments, the curation tool is utilized to curate concepttopicality, whereby a curator is shown media content items for “substories” of a main story and asked media content items are relevant tothe topic of the main story.

Additionally, for some embodiments, the curation tool is utilized tocurate breaking news, whereby curation involves: the headline of thestory; place name; bounding box for the place; whether it is a mixedstory; whether the story is in non-English; whether the story containsoffensive material; whether the story is low quality; newsworthiness;keywords for the story; context cards forplaces/people/organizations/events related to the story; or related newsURLs.

In FIG. 35, the graphical user interface illustrates an example of whatactual data that gets stored for a dynamic story looks like.Specifically, this is a dynamic story of a band called Majid Jordanplaying at the Fonda Theatre. In FIG. 36, entity information for theplace is provided on the left, and entity information for the band isprovided on the right.

FIG. 37 is a flow diagram illustrating an example request flow in asocial media content curation tool in accordance with some embodimentsdescribed in this document.

FIG. 38 is a flow diagram illustrating an example method 3800 forgenerating dynamic collections of media content items (e.g., stories) inaccordance with some embodiments described in this document. The method3800 begins at block 3802, with clusters being created from a pluralityof media content items, thereby grouping media content items intocohesive clusters, where cohesiveness can be in time, space, visualfeatures, topics, and the like. For some embodiments, various types ofclusters are created for different types of collections of media contentitems.

For example, geo-time clusters are created. Geo-time clusters cancapture collections of media content items (e.g., stories) that aremostly around an event and are cohesive in time and location. A range ofsimilarity signals on top of time/location may be taken into account toassure media content items that are clustered tell the same collectionof media content items (e.g., story). These similarity signals include,but are not limited to: visual similarity; caption (salient term)similarity; topic similarity (e.g., two (sets of) media content itemsthat are fashion media content items and are close by are more likely tobe clustered together); place similarity (e.g., similarity of placeentity assigned to two (sets of) media content items is considered astrong signal for considering clustering them together); and On DemandGeoFilter (ODG) similarity.

In another example, topic (“chatter”) clusters are created. Topicclusters can be cohesive around a theme or topic but are not necessarilytied to a specific time and location. In some instances, topicclustering is accomplished in two steps. In the first step, single topicclustering is performed by clustering all media content items (e.g., inthe past 24 hours) containing a topic for each distinct topic (e.g.,currently identified as a distinct significant caption term or visuallabel but expandable to any rule expressed in terms of features of themedia content items). A score profile is created during the first step.Components of the score profile can include, without limitation, theindependence of topic (reflecting how sufficient the topic is todescribe the majority of media content items); the popularity of thetopic in terms of the number of posted media content items that arerelevant to the topic; the freshness of the topic measured using the agedistribution of media content items about the topic; the novelty of thetopic measured by comparing the volume of fresh media content itemsabout this topic compared to the historical volume for this topic; theglobalness of the topic that reflects how geographically spread themedia content items relevant to the topic are; and the quality anddescriptiveness of the collection of media content items (e.g., story)for the topic that reflects the quality (e.g., including media quality,user/creator quality, etc.) of media content items shown for that topicand how well they tell the collection of media content items (e.g.,story) for that topic (for e.g. video media content items are generallyconsidered more descriptive than image media content items). Based onall the score components, an overall score is generated to represent howinteresting the topic cluster is. The score components along with theoverall score are used in merging similar topics during the second stepand also for ranking topic collections of media content items (e.g.,stories) in user interface presented to users (e.g., the topicscarousel).

In the second step, similar topics are merged. Depending on what topicsare trending, the trending topic collections of media content items(e.g., stories) can be correlated and in some cases redundant. Hence, inthe second stage of topic cluster generation we merge similar topicstogether. Given the cost of topic clustering/merging, for someembodiments, full merging is only performed for the top topic clusters(based on the score computed in the first step) and join the other topicclusters to the merged top clusters. The merging of two topiccollections of media content items (e.g., stories) can be done based ona variety of similarity criteria including, without limitation: synonymmatch between two topics; ngram similarity between terms associated withtwo topics; context similarity between based on the aggregate salientterms of media content items associated with two topics; contextsimilarity based on similarity between users posting media content itemsabout the two topics; and language mismatch is used as a negativesimilarity signal.

At block 3804, initial dynamic collections of media content items (e.g.,stories) are created from the clusters. This may be accomplished byfinding collections of media content items (e.g., stories) within thecluster and inferring attributes of the collections including placename, caption, topic, highlight, repeated-ness, and the like. Givenclusters of media content items (from block 3802), during block 3804 themethod 3800 finds the collections of media content items in each clusterand determines what each collection of media content items (e.g., story)is about. For various embodiments, block 3804 involves: identifyingsub-collections (e.g., sub-stories) within the cluster (e.g., based ontime, geo, visual, and cohesiveness); breaking the over-clustered eventsinto multiple collections of media content item (e.g., stories); findinga caption for each collection (e.g., story); find a place name for eachcollection; finding a category of the collection (e.g., concert, game,and fashion); creating a highlight for each dynamic collection; anddetermining whether a collection is a repeated happening. Creatinghighlight for each dynamic collection involves: finding the highlight atthe order viewable by the user; and finding a hero (e.g., cover mediacontent item) for the highlight. The highlight is selected based onscoring, which may have the following factors/criteria considered:cohesive and representative of the collection; has high collectiontelling score; has high creative quality score; generally preferred tobe a video media content item rather than an image (e.g., with theexception of the collection being image seeking); and does not containlow quality media quality relative to all the candidates for highlightselection. Media quality can be calculated from the input signalsdescribed in this document and may be generally aimed to removeblack/dark, very bright with no visual content and shaky media contentitems. Find a hero for the highlight may consider the followingfactors/criteria: has a high score in highlight scoring; representativein visual features relative to what the collection is about; not aselfie media content item; and does not have a caption on the mediacontent item.

At block 3806, initial dynamic collections are post-processed andreconciled, which generated final dynamic collections. For someembodiments, post-processing comprises: merging collections with similarcaption or place; de-duping collections that are too similar (e.g.,shared media content item volume, or caption/place similarity);filtering bad clusters (e.g., based on no place name, no collections,and topic based filtering); detecting ancestors (e.g., by generatingstable ids to establish continuity between runs [deduping with the past]and keeping a running history of the cluster); and aggregating stats,such as generated cluster sources and sizes (to track pipeline health)and top salient terms and concepts.

FIG. 39 is a screenshot illustrating an example graphical user interfaceincluding an emoji shortcuts section for a pre-typing view in accordancewith some embodiments described in this document. As shown, the emojishortcuts section comprises of a horizontal scroll of emojis in pillsthat, after selection (e.g., user finger tap), take a user into a searchexperience as if they had typed the word that's associated with thatemoji. This may be accomplished by an associated word being entered intothe search box when an emoji is tapped by the user. Examples ofemoji-word association include, without limitation: a tongue with theword “restaurants”; dolphin with the word “dolphin”; a Japanese flagwith the word japan”; a basketball with the word “nba”; statue ofliberty with the word “new york”; a drink with the word “bars”; and atable tennis paddle with the word “table tennis.” Example types of emojishortcuts include, without limitation, locations (e.g., “Japan”, “NewYork”), categories (e.g., “Bars” and “Restaurants”), topics (e.g.,“dolphin” and “table tennis”); and events. The listing of emoji pillscan be presented as a carousel such that a user can swipe in onedirection and loop back to the opposite end of the listing.

With respect to some embodiments, ranking emojis in the listing of emojipills is accomplished by two components: an offline (e.g., at storybuilding time) computation of a list of relevant emojis for each story;and an online (e.g., when producing the pre-type search page),generation of an emoji listing (e.g., carousel) based on the most recentset stories (e.g., K stories). The offline computing of the list ofrelevant emojis may comprising using a set of signal including, withoutlimitation: story captions (e.g., text captions from the media contentitems in the story) and the emojis the users typed in their captions;top salient story terms; story category; and derived aggregate data,such as term IDF tables, an emoji to terms model, and a term to emojimodel. With respect to the online generation of an emoji listing, foreach story in the most recent set of stories, the following can beretrieved: story id; display caption; top story salient terms;geographic location (e.g., lat/long); raw media content item count;emoji annotation; and story timestamp (e.g., 95-percentile storytimestamp). Emojis are extracted from all the stories retrieved,de-duped (for the similar ones), and ranked based on freshness andvolume of the underlying stories. For each emoji extracted, the captionof the best ranked story (e.g., by volume and freshness) that has thatemoji is assigned as a search query associated with the emoji. In someinstances, the top set of emojis (e.g., top 15) are presented to a userin an emoji carousel.

FIG. 40 is a screenshot illustrating an example graphical user interfaceincluding a topics section for a pre-typing view in accordance with someembodiments described in this document. As shown, the topics sectioncomprises a carousel of stories each titled by a topic and eachconsisting of a set of media content items relevant to that topic. Forsome embodiments, topic stories are detected and ranked based on a setof signals including, without limitation: independence; popularity;freshness; novelty; globalness; proximity; quality; and descriptiveness.The independence of the topic may reflect how sufficient the topic is todescribe the majority of media content items associated to that topic.For instance, topic A is not independent if, among the media contentitems containing the attributes (e.g., terms, labels, etc.) of A,majority of them are dominated by attributes (e.g., terms, labels, etc.)from topics other than A. The popularity of the topic may be in terms ofthe number of posted media content items that are relevant to the topic.The freshness of the topic may be measured using the age distribution ofmedia content items about the topic. The novelty of the topic may bemeasured by comparing the volume of fresh media content items about thistopic compared to the historical volume for this topic. The globalnessof the topic may reflect how geographically spread the media contentitems relevant to the topic are. The proximity of the topic may reflecthow relevant the topic is to where user is. The quality anddescriptiveness of the story for the topic may reflect the quality(e.g., including media quality, user/creator quality, etc.) of mediacontent items shown for that topic and how well they tell the story forthat topic (e.g. video media content items are generally considered moredescriptive than image media content items). For some embodiments,diversity of the topics carousel is secured by making sure thatredundant topics are merged together in the online story generationpipeline.

FIG. 41 is a screenshot illustrating an example graphical user interfaceincluding a live stories section for a pre-typing view in accordancewith some embodiments described in this document. As shown, the livestories section comprises a carousel of stories each corresponding to anevent each titled by a caption and location (e.g., city) for the eventand each containing a set of media content items showing the highlightsof the event. For some embodiments, live stories are selected and rankedbased on a set of signals including, without limitation: freshness;proximity; diversity; and quality and descriptiveness. Freshnessreflects how recently the story was posted or updated. For the livestories section, there is a preference that events are identified to belive as compared to the stories for events that have already ended, andif the events are finished, there may be a preference to list storiesfrom the same day compared to older events. Proximity reflects proximityof the event to the user. For the live stories section, there is apreference for stories that are within the same city as the user andapply a demotion to the stories based on their distance to the user.Diversity is secured by making sure that stories shown in the carouselcover a variety of categories of events including, without limitation,sports, concerts, fashion, and politics. The quality and descriptivenessof the story reflects the quality of media content items shown for theevent and how well they tell the story for the event.

FIG. 42 is a screenshot illustrating an example graphical user interfaceincluding a media content item tabs section for a pre-typing view inaccordance with some embodiments described in this document. As shown,the media content item tabs comprise a table of multiple tabs eachcontaining a list of stories within a certain category. Examples ofthose categories include, without limitation, breaking now; attractions;concerts; games(sports); fashion; politics; parties; filters; feels;highlights; and pets. Breaking now provides a list of live stories goingon right now, can include news stories, and can be sorted based onnewsyness, proximity to the user, or media content item/user volume ofthe stories. Attractions provides a list of stories about attractionsclose to the user. Attraction stories can be ones that are identified tobe repeating almost daily with similar content (e.g., in terms of mediacontent item captions and salient terms) and similar volume in the sameplace (e.g., stories like DISNEYLAND, UNIVERSAL STUDIO, and GriffithObservatory). Concerts provides a list of concerts sorted by freshness,proximity, volume, or quality of the stories. The aggregate salientcaption terms and visual labels of story media content items may be usedto identify whether a story is a concert or not. Games provides a listof sports stories sorted by freshness, proximity, volume, or quality ofthe stories. Similar to concerts, aggregated salient caption terms andvisual labels of story media content items may be utilized to identifythe category/topic of the event and further a set of third party sportsevents are used to canonicalize the caption of the sports games (e.g.,from Go <team-name> which is a media content item based caption of thestory to team A vs. team B which is the canonicalized name of theevent). Fashion events provide a list of fashion stories that arecategorized and ranked, possibly similar to concerts and games. Politicsevents provides a list of political stories that are categorized andranked, possibly similar to concerts, games and fashion events. Partiesprovides a list of party-related stories that are categorized andranked, possibly similar to concerts, games, fashion events and politicsevents. Filters provides a list of stories each representing mediacontent items from a certain close by geo filter, and may be sorted byfreshness, proximity, quality, or density of the story. Feels provides alist of stories from nearby media content items each around a certainsentiment (e.g., happy, sad, shocking, etc.). A set of keywords andemojis per feeling may be used to identify the association of a mediacontent item to a feeling. Highlights provides an overview of majornearby events in the past week. Pets provides a list of stories eacharound media content items about a certain pet, and the association of amedia content item to a certain pet category may be accomplished basedon the visual labels of the media content item.

According to various embodiments, dynamic stories are generated forspecific places and large locations, merged from multiple data sourcesin real time for media content item submitted live, and indexed tosupport place and location various sections described in this document:pre-typing views, such as current place, around me carousel; andpost-typing cards, such as for queries of specific places or largerareas like neighborhood, city, universities, and the like. A backend ofbuilding dynamic stories for places may be shared across all the views,and a ranking module (e.g., light weighted) may be applied to each viewindividually. Core components of the backend comprises, for example:media content item-to-place assignment; place story generation; locationstory generation; and online ranking. Media content item-to-placeassignment assigns each media content item (e.g., that is summited tolive) to the best matched place using a probabilistic model of a mixedof signals in a real-time data pipeline. Place story generationregenerates a dynamic story using latest media content items for eachplace assigned to the media content items. Location story generationmixes dynamic stories from local places, current events and trendingtopics, and may do so for larger areas where media content items arerefreshed frequently. For both pre-typing/post-typing views, a rankingmodule is applied to all the relevant stories in the context (e.g., plusadditional place data, from Google API, etc.) to select the best card toshow in real-time.

With respect to media content item-to-place assignment, each mediacontent item submitted to live may be taken together with other mediacontent items within a fixed time window, and may be assigned to thebest matched place using a probabilistic model. The candidate places canbe from multiple data sources, including: GOOGLE Place Response passedby mobile clients using GOOGLE's Place Detection API; GOOGLE PlaceResponse from querying GOOGLE Geocoding/Reverse Geocoding APIs andGOOGLE Place API, in the backend; a geo-indexed Open Street Map data;existing geo-filter data; and geo-fences data through third partypartnerships. For some embodiments, the assignment is accomplishedthrough a probabilistic approach, which utilizes a mixed of cues orsignals. Example signals can include, without limitation; geo signals;temporal signals, text signals, visual signals, and quality signals.

Geo signals can comprise associated GPS data with each media contentitem, and the known lat/long of each place candidate. The accuracy andquality of GPS data may be similar to the error range to many smallplaces in interest. The distances of a list of candidates within arelative larger range may need to be taken into consideration in astatistical fashion. Temporal signals can comprise the opening hour, andhistorical temporal activity pattern, of a place, which can be used tomatch against the media content item based on its location timestamp.Text signals can comprise place name and other text properties (likedescription, salient term profile, etc.) which can be used to match thetext properties of the media content item, including caption, mediacontent item labels learned by deep learning models from image content.Visual signals can comprise simplified visual models, such asindoor/outdoor/vehicle, which can help to match certain place typeswhich has obvious indoor property. An alternative approach can use allprevious annotated media content items to learn visual features of theplace, and use it to compute the distance from the visual features ofthe specific place. Quality signals can comprise the quality of theplace like ratings, previous activity, daily volume, and the like, whichcan indicate the media content item has a higher chance of being takenthere. A prior of probability on each place may be pre-computed offlineusing foot traffic data (e.g., user activity geo-temporal map).

With respect to place story generation, a dynamic story of the place isgenerated using the media content items that have been assigned to it.For some embodiments, dynamic stories are constantly regenerated forplaces when fresh media content items are submitted and assigned tothem. The ranking algorithm that selects media content items for theplace dynamic story may consider: freshness of the media content items(e.g., for specific places like bars, the story will give users a sensewhat is happening right now at the place); events at the place (e.g.,latest events (with lots of media content items at similar time) will beincluded in the dynamic story); quality of the media content items(e.g., a flexible quality measure, that combines user score, media scoreand other factors, may be used to filter low-quality media contentitems); and cohesiveness of the story (e.g., the algorithm tries tobalance between the vibe and theme of the place). Additionally, a placeprofile (e.g., including salient terms, visual features, etc.) can beused to select representative media content items based on historicalmedia content items.

With respect to location story generation, a dynamic story for a largerlocation, like city, neighborhood, university, etc. is generated bycombining sub-stories from local specific places, live events, andtopical events. For each dynamic story, a place is annotated using itsgeo bounding box. The location story data pipeline may constantlyaggregate all the place stories and latest live events and topicalstories according to the neighborhood and city info of the place.Sub-stories may be selected to show the vibe and live atmosphere in thelocation, by a ranking module that takes into account various metrics ofeach story: volume, freshness, story quality, etc.

With respect to online ranking, for each view supported by placestories, a ranking module is applied online based on retrieved candidatestories. For instance, for current place, the nearest place story (alist of nearest place stories) is be retrieved using the user's currentlocation, which will be compared to GOOGLE Current Place API todetermine which card type to show. For around me carousel, a batch ofcandidate nearby place stories, and event stories are ranked based ontheir scores, including freshness, quality, volume, and the like. Forpost-typing queries, the place/location stories are queried when a usertypes the keyword. The candidate stories can be scored to determine ifthe location/place card, which contains the place/location story, shouldshow up.

FIG. 43 is a screenshot of an example graphical user interface thatpresents an around me section of a pre-typing view in accordance withsome embodiments described in this document. As shown, the around mesection includes stories for specific places and large locations inclose proximity to the user.

As noted in this document, a post-type result is generated while theuser is typing in the search box. When the user starts typing, a list ofstories that are topical for queries starting or very similar to whatthe user has typed begin to show up. The results may be rendered indifferent forms including, for example: a story list; a hero card withsee more button; or a stand-out hero card. What queries are shown candepend both on the similarity of query to user input and alsosignificance/quality of the story topical for the query. In someinstances, a hero card appears when there is no ambiguity about theintent of the user, and the shown result is a single story that includesthe highlights of the most relevant stories around the query in a rankedorder. The ranking of story highlights within the hero card isaccomplished based on a variety of signals including, withoutlimitation: centrality and topicality of the query for the story;proximity; freshness; popularity (e.g., reflected by the number of mediacontent items in the corresponding story); and quality including themedia quality, diversity of media content items and descriptiveness ofmedia content items.

FIG. 44 is a screenshot of an example graphical user interface thatpresents a more stories section of a post-typing view in accordance withsome embodiments described in this document. The more stories sectionprovides a set of dynamic stories related to the current story typed.These stories may not be exactly what user searched for but may beperipheral stories around the main story. The more stories sectioninvolves a trigger condition where the section is only triggered whenthere is already a selected hero card (i.e., search result provided).One there is a selected hero card, it is analyzed to generate morestories for the given story provided by the hero card. Input signalsutilized to populate the more stories section includes, withoutlimitation: captions and visual labels of media content items which arethe results for the user query (e.g., in the hero card) and expandsearch queries using historical concurrence; location of the user whenhe/she issues the query or location within the query; and type of theresults for the user query. Examples of types can include a live event(e.g., concert or games), concept (e.g., ski or Christmas), place (e.g.,1 Oak or Staple Center), or location (e.g., London or North Beach).

The types of queries and types of more stories presented in the sectionmay be based on the hero type used to present the results. For example,more stories presents live events query stories for theneighborhood/city, other nearby live events, earlier events in the samevenue. More stories section presents concept query stories with similarfeel like. So, for instance, a hero card presenting ski would have amore stories section presenting snowboarding, a hero card presentingwinter would have a more stories section presenting snow fall, and ahero card presenting Christmas would have a more stories sectionpresenting gift unwrapping. If the concept has local significance, otherlocally significant stories may be presented by more stories, such as ahero card presenting Santa Con would have a more stories sectionpresenting Santa Con SF. More stories section presents place querystories to show stories from same place type (e.g.,bars/restaurants/clubs), which can include stories about theneighborhood and city as well as story about nearby attractions. Morestories section presents location query stories about popular places andattractions in the location as well as stories about sister locations(e.g., Venice presents Santa Monica Pier).

A related search section involves queries which can be viewed as pivotsfor exploration around a different dimension from the current searchquery. The related search section queries may be complementary to thequeries being performed for the more stories section. While a morestories section provides peripheral information related to the userquery in the same context in the form of a story, the related searchsection queries can provide user a way to pivot into a new context inthe form of new query. According to some embodiments, the signals andinputs used for creating the related searches section are similar tothat of more stories.

Query suggest provides suggestion as user type in queries, based on bothuser context and our index of stories. Through autocomplete, userefforts to type a whole search query can be reduced, and the user canalso be guided to search the best content. The index for the querysuggest is based on, for example: information from stories generatedfrom other pipelines that have high probability to become a query, suchas location, captions etc.; and top queries from logs. On the servingside, the context of client is used to provide the best guess of a userintended search query. The context includes, for example: the partialquery user is currently typing; the Location, and the language.

Breaking news section provides a set of dynamic stories intended tocapture newsworthy events as they happen and, as such, it may not appearin every pre-type search. Breaking news may only display when there is asufficiently important story currently ongoing or in the recent past.Breaking news section is created through a pipeline similar to the onethat creates dynamic stories for the live story carousel. Like for livestory, the pipeline runs the exact same logic on a smaller set of inputmedia content items. Unlike live story, the pipeline for breaking newsmay run on smaller window of time (e.g., last 30 minutes of mediacontent items).

For breaking news section, media content items from submit-to-live comeas input to the story generation pipeline, and these media content itemshave captions, location, etc. From these media content items, storiesare generated, which are groupings of media content items associatedwith a location, a descriptive caption, a cover media content item, or aselection of media content items to show to the user. For ranking, thestories are assigned a ‘newsiness score,’ which captures both therelevance and significance of the story. By the newsiness score, storiescan be distinguished between where users appear to be discussing anewsworthy topic, as well as those that have sufficient media contentitem volume and diversity of contributing users. According to someembodiments, in order to show a story in the breaking news section, itsnewsiness score must exceed a threshold. Among those stories whosescores exceed the threshold, the ordering is decided based on thenewsiness score, the story freshness, and the proximity of the story tothe user. From the client perspective, the breaking news sectionspresents stories in similar many as other dynamic stories in the client.As such, stories in the breaking news section can have a captionalgorithmically generated from the user captions, a cover media contentitem, and a list of media content items to display when the story isselected.

People search and related people sections provide stories from popularusers. A popular user may be defined as an account with public storyprivacy and at least five thousand followers. Popular users may havenames, categories, and associated locations curated. According to someembodiments, a search index is created for each popular user/accountthat contains the following: username; display name; any names forpopular users curated; categories the popular user is associated with(e.g., NBA, hip hop, fashion, yoga) and which may be curated; associatedlocations curated; and official story indicator (e.g., emoji) ifapplicable. A popular user may appear in the search results for a matchon: any of the above names with no minimum prefix length; any of theabove categories; or on any other popular user that is contained intheir same category.

FIG. 45 is a flow diagram illustrating an example method 4500 forgenerating collections of media content items in accordance with someembodiments described in this document. The method 4500 begins at block4502, with a set of annotations being generated for a particular mediacontent item, in a plurality of media content items, based on metadataof the particular media content item. For some embodiments, the set ofannotations including information inferred or derived from the metadata.

At block 4504, the particular media content item is associated with acluster based on the set of annotations generated for the particularmedia content item at block 4502. For some embodiments, the clustergroups together certain media content items, within the plurality ofmedia content items, based on a relationship between at least oneannotation of each of the certain media content items. The relationshipmay relate to, for example, geographical proximity, time proximity,similarity of caption terms, similarity of a place, similarity of avisual feature, similarity of an event, similarity of a metric, orsimilarity of a user statistic. For some embodiments, the relationshipbetween the annotations of two or more media content items reflects thecohesiveness of those media content items with respect to one or moredimensions (e.g., time, space, visual features, topics, etc.). At block4506, a collection of media content items (e.g., story), associated withthe cluster, is determined (e.g., identified) from the plurality ofmedia content items. At block 4508, a set of attributes is determined,for the collection of media content items, based on at least oneannotation of a media content item included in the collection of mediacontent items (determined at block 4506). At block 4510, the collectionof media content items is stored with the set of attributes determinedat block 4508.

Determining the collection of media content items from the plurality ofmedia content items may comprise determining a plurality of collectionsof media content items. According to some embodiments, the plurality ofcollections is reconciled by merging at least two collections of mediacontent items, in the plurality of collections, based on a similaritybetween at least one attribute of each of the at least two collectionsof media content items. For some embodiments, the plurality ofcollections is reconciled by removing a particular collection of mediacontent items, from the plurality of collections, based on a similaritybetween at least one attribute of the particular collection of mediacontent items and another collection of media content items in theplurality of collections. For some embodiments, the plurality ofcollections is filtered to produce a filtered plurality of collectionsof media content items that includes media content items having at leasta minimum set of attributes.

For various embodiments, determining the collection of media contentitems from the plurality of media content items comprises selecting ahighlight media content item, from the collection of media contentitems, to represent the collection of media content items based on a setof scores associated with the highlight media content item.

With respect to searching, the method may further comprise: indexing theplurality of collections of media content items; searching the pluralityof collections of media content items based on a search request from aclient device to identify a set of collections (e.g., collections ofmedia content items) of interest; and providing search response to theclient device based on the searching, the search response identifyingthe stored collection of media content items as part of the set ofcollections of interest. The search response may provide access to thestored collection of media content items through at the client device.

The set of attributes may include at least one of a caption associatedwith the collection of media content items, a place name associated withthe collection of media content items, and a category associated withthe collection of media content items. The set of annotations maycomprise at least one annotation relating to a time, a geographiclocation, a place, a topic, a visual feature, a caption, an event, amedia quality of the particular media content item, or a user statisticrelating to the particular media content item.

FIG. 46 is a block diagram illustrating an example software architecture4606, which may be used in conjunction with various hardwarearchitectures in this document described. FIG. 46 is a non-limitingexample of a software architecture and it will be appreciated that manyother architectures may be implemented to facilitate the functionalitydescribed in this document. The software architecture 4606 may executeon hardware such as machine 4700 of FIG. 47 that includes, among otherthings, processors 4704, memory 4714, and I/O components 4718. Arepresentative hardware layer 4652 is illustrated and can represent, forexample, the machine 4700 of FIG. 47. The representative hardware layer4652 includes a processing unit 4654 having associated executableinstructions 4604. Executable instructions 4604 represent the executableinstructions of the software architecture 4606, including implementationof the methods, components and so forth described in this document. Thehardware layer 4652 also includes memory and/or memory/storage modules4656, which also have executable instructions 4604. The hardware layer4652 may also comprise other hardware 4658.

In the example architecture of FIG. 46, the software architecture 4606may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 4606may include layers such as an operating system 4602, libraries 4620,applications 4616 and a presentation layer 4614. Operationally, theapplications 4616 and/or other components within the layers may invokeapplication programming interface (API) calls 4608 through the softwarestack and receive messages 4612 in response to the API calls 4608. Thelayers illustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile or specialpurpose operating systems 4602 may not provide frameworks/middleware4618, while others may provide such a layer. Other softwarearchitectures may include additional or different layers.

The operating system 4602 may manage hardware resources and providecommon services. The operating system 4602 may include, for example, akernel 4622, services 4624 and drivers 4626. The kernel 4622 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 4622 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 4624 may provideother common services for the other software layers. The drivers 4626are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 4626 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 4620 provide a common infrastructure that is used by theapplications 4616 and/or other components and/or layers. The libraries4620 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 4602 functionality (e.g., kernel 4622,services 4624 and/or drivers 4626). The libraries 4620 may includesystem libraries 4644 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 4620 may include API libraries 4646 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 4620 may also include a wide variety of otherlibraries 4648 to provide many other APIs to the applications 4616 andother software components/modules.

The frameworks/middleware 4618 (also sometimes referred to asmiddleware) provide a higher-level common infrastructure that may beused by the applications 4616 and/or other software components/modules.For example, the frameworks/middleware 4618 may provide various graphicuser interface (GUI) functions, high-level resource management,high-level location services, and so forth. The frameworks/middleware4618 may provide a broad spectrum of other APIs that may be utilized bythe applications 4616 and/or other software components/modules, some ofwhich may be specific to a particular operating system 4602 or platform.

The applications 4616 include built-in applications 4638 and/orthird-party applications 4640. Examples of representative built-inapplications 4638 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 4640 may include anapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 4640 may invoke the API calls 4608 provided bythe mobile operating system (such as operating system 4602) tofacilitate functionality described in this document.

The applications 4616 may use built-in operating system functions (e.g.,kernel 4622, services 4624 and/or drivers 4626), libraries 4620, andframeworks/middleware 4618 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such aspresentation layer 4614. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 47 is a block diagram illustrating components of a machine 4700,according to some example embodiments, able to read instructions 4604from a machine-readable medium (e.g., a machine-readable storage medium)and perform any one or more of the methodologies discussed in thisdocument. Specifically, FIG. 47 shows a diagrammatic representation ofthe machine 4700 in the example form of a computer system, within whichinstructions 4710 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 4700 toperform any one or more of the methodologies discussed in this documentmay be executed. As such, the instructions 4710 may be used to implementmodules or components described in this document. The instructions 4710transform the general, non-programmed machine 4700 into a particularmachine 4700 programmed to carry out the described and illustratedfunctions in the manner described. In alternative embodiments, themachine 4700 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine4700 may operate in the capacity of a server machine or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 4700 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smart phone, amobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machine4700 capable of executing the instructions 4710, sequentially orotherwise, that specify actions to be taken by machine 4700. Further,while only a single machine 4700 is illustrated, the term “machine”shall also be taken to include a collection of machines 4700 thatindividually or jointly execute the instructions 4710 to perform any oneor more of the methodologies discussed in this document.

The machine 4700 may include processors 4704, memory memory/storage4706, and I/O components 4718, which may be configured to communicatewith each other such as via a bus 4702. Processors 4704 may comprise ofa single processor or, as shown, comprise of multiple processors (e.g.,processors 4708 to 4712). The memory/storage 4706 may include a memory4714, such as a main memory, or other memory storage, and a storage unit4716, both accessible to the processors 4704 such as via the bus 4702.The storage unit 4716 and memory 4714 store the instructions 4710embodying any one or more of the methodologies or functions described inthis document. The instructions 4710 may also reside, completely orpartially, within the memory 4714, within the storage unit 4716, withinat least one of the processors 4704 (e.g., within the processor 4708'scache memory), or any suitable combination thereof, during executionthereof by the machine 4700. Accordingly, the memory 4714, the storageunit 4716, and the memory of processors 4704 are examples ofmachine-readable media.

The I/O components 4718 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 4718 that are included in a particular machine 4700 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 4718 may include many other components that are not shown inFIG. 47. The I/O components 4718 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 4718may include output components 4726 and input components 4728. The outputcomponents 4726 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 4728 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 4718 may includebiometric components 4730, motion components 4734, environmentcomponents 4736, or position components 4738 among a wide array of othercomponents. For example, the biometric components 4730 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 4734 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 4736 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 4738 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 4718 may include communication components 4740operable to couple the machine 4700 to a network 4732 or devices 4720via coupling 4722 and coupling 4724 respectively. For example, thecommunication components 4740 may include a network interface componentor other suitable device to interface with the network 4732. In furtherexamples, communication components 4740 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 4720 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 4740 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 4740 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components4740, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

It will be understood that various components used in this context(e.g., system components) refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A hardware component is a tangible unitcapable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor 4708 or agroup of processors 4704) may be configured by software (e.g., anapplication 4616 or application portion) as a hardware component thatoperates to perform certain operations as described in this document. Ahardware component may also be implemented mechanically, electronically,or any suitable combination thereof. For example, a hardware componentmay include dedicated circuitry or logic that is permanently configuredto perform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor 4708 or other programmable processor 4708.Once configured by such software, hardware components become specificmachines (or specific components of a machine 4700) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 4704. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described in thisdocument. Considering embodiments in which hardware components aretemporarily configured (e.g., programmed), each of the hardwarecomponents need not be configured or instantiated at any one instance intime. For example, where a hardware component comprises ageneral-purpose processor 4708 configured by software to become aspecial-purpose processor, the general-purpose processor 4708 may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor 4708 or processors 4704,for example, to constitute a particular hardware component at oneinstance of time and to constitute a different hardware component at adifferent instance of time. Hardware components can provide informationto, and receive information from, other hardware components.Accordingly, the described hardware components may be regarded as beingcommunicatively coupled. Where multiple hardware components existcontemporaneously, communications may be achieved through signaltransmission (e.g., over appropriate circuits and buses) between oramong two or more of the hardware components. In embodiments in whichmultiple hardware components are configured or instantiated at differenttimes, communications between such hardware components may be achieved,for example, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described inthis document may be performed, at least partially, by one or moreprocessors 4704 that are temporarily configured (e.g., by software) orpermanently configured to perform the relevant operations. Whethertemporarily or permanently configured, such processors 4704 mayconstitute processor-implemented components that operate to perform oneor more operations or functions described in this document. As used inthis document, “processor-implemented component” refers to a hardwarecomponent implemented using one or more processors 4704. Similarly, themethods described in this document may be at least partiallyprocessor-implemented, with a particular processor 4708 or processors4704 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 4704or processor-implemented components. Moreover, the one or moreprocessors 4704 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 4700including processors 4704), with these operations being accessible via anetwork 4732 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an application program interface (API)). Theperformance of certain of the operations may be distributed among theprocessors 4704, not only residing within a single machine 4700, butdeployed across a number of machines 4700. In some example embodiments,the processors 4704 or processor-implemented components may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theprocessors 4704 or processor-implemented components may be distributedacross a number of geographic locations.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“EMPHEMERAL MESSAGE” in this context refers to a message that isaccessible for a time-limited duration. An ephemeral message may be atext, an image, a video and the like. The access time for the ephemeralmessage may be set by the message sender. Alternatively, the access timemay be a default setting or a setting specified by the recipient.Regardless of the setting technique, the message is transitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but is not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described in this document. Accordingly, a“machine-readable medium” refers to a single storage apparatus ordevice, as well as “cloud-based” storage systems or storage networksthat include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described in this document. A hardware componentmay also be implemented mechanically, electronically, or any suitablecombination thereof. For example, a hardware component may includededicated circuitry or logic that is permanently configured to performcertain operations. A hardware component may be a special-purposeprocessor, such as a Field-Programmable Gate Array (FPGA) or anApplication Specific Integrated Circuit (ASIC). A hardware component mayalso include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware component may include software executed by a general-purposeprocessor or other programmable processor. Once configured by suchsoftware, hardware components become specific machines (or specificcomponents of a machine) uniquely tailored to perform the configuredfunctions and are no longer general-purpose processors. It will beappreciated that the decision to implement a hardware componentmechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations. Accordingly, the phrase“hardware component” (or “hardware-implemented component”) should beunderstood to encompass a tangible entity, be that an entity that isphysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described in this document. Consideringembodiments in which hardware components are temporarily configured(e.g., programmed), each of the hardware components need not beconfigured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information). The various operationsof example methods described in this document may be performed, at leastpartially, by one or more processors that are temporarily configured(e.g., by software) or permanently configured to perform the relevantoperations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described in thisdocument. As used in this document, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described in this document may be at leastpartially processor-implemented, with a particular processor orprocessors being an example of hardware. For example, at least some ofthe operations of a method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

Where a phrase similar to “at least one of A, B, or C,” “at least one ofA, B, and C,” “one or more A, B, or C,” or “one or more of A, B, and C”is used, it is intended that the phrase be interpreted to mean that Aalone may be present in an embodiment, B alone may be present in anembodiment, C alone may be present in an embodiment, or that anycombination of the elements A, B and C may be present in a singleembodiment; for example, A and B, A and C, B and C, or A and B and C.

As used in this document, the term “or” may be construed in either aninclusive or exclusive sense. Moreover, plural instances may be providedfor resources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

It will be understood that changes and modifications may be made to thedisclosed embodiments without departing from the scope of the presentdisclosure. These and other changes or modifications are intended to beincluded within the scope of the present disclosure.

What is claimed is:
 1. A method comprising: generating, by one or moreprocessors, a set of annotations for a particular media content item, ina plurality of media content items, based on metadata of the particularmedia content item; associating, by the one or more processors, theparticular media content item with a cluster based on the set ofannotations generated for the particular media content item, the clustergrouping together certain media content items within the plurality ofmedia content items based on a cohesiveness of geographic location andtime; generating, by the one or more processors, a collection of mediacontent items from the plurality of media content items based on thecluster, the collection comprising two or more media content items fromthe plurality of media content items that are associated with thecluster, the collection being separate from the cluster; determining, bythe one or more processors, a set of attributes for the collection ofmedia content items based on at least one annotation of a media contentitem included in the collection of media content items; storing, on anew index of a search service index and by the one or more processors,the collection of media content items in association with the set ofattributes, the storing comprising swapping a current index of thesearch service index with the new index such that a previous version ofthe collection stored on the current index is replaced by the collectionstored on the new index; searching, on the new index of the searchservice index and by the one or more processors, a plurality ofcollections of media content items based on a search request from aclient device to identify a set of collections of interest, theplurality of collections of media content items including the storedcollection of media content items; and providing, by the one or moreprocessors, search response to the client device based on the searching,the search response identifying the stored collection of media contentitems as part of the set of collections of interest.
 2. The method ofclaim 1, wherein the determining the collection of media content itemsfrom the plurality of media content items comprises determining theplurality of collections of media content items, the method furthercomprising: reconciling, by the one or more processors, the plurality ofcollections by merging at least two collections of media content items,in the plurality of collections, based on a similarity between at leastone attribute of each of the at least two collections of media contentitems.
 3. The method of claim 1, wherein the determining the collectionof media content items from the plurality of media content itemscomprises determining the plurality of collections of media contentitems, the method further comprising: reconciling, by the one or moreprocessors, the plurality of collections by removing a particularcollection of media content items, from the plurality of collections,based on a similarity between at least one attribute of the particularcollection of media content items and another collection of mediacontent items in the plurality of collections.
 4. The method of claim 1,wherein the determining the collection of media content items from theplurality of media content items comprises determining the plurality ofcollections of media content items, the method further comprising:filtering, by the one or more processors, the plurality of collectionsof media content items to produce a filtered plurality of collections ofmedia content items that includes media content items having at least aminimum set of attributes.
 5. The method of claim 1, wherein thedetermining the collection of media content items from the plurality ofmedia content items comprises: selecting a highlight media content item,from the collection of media content items, to represent the collectionof media content items based on a set of scores associated with thehighlight media content item.
 6. The method of claim 1, furthercomprising: prior to searching the plurality of collections of mediacontent items, indexing, by the one or more processors, the plurality ofcollections of media content items.
 7. The method of claim 1, whereinthe set of attributes include at least one of a caption associated withthe collection of media content items, a place name associated with thecollection of media content items, and a category associated with thecollection of media content items.
 8. The method of claim 1, wherein theat least one annotation relates to a particular time, a particulargeographic location, a place, a topic, a visual feature, a caption, anevent, a media quality of the particular media content item, or a userstatistic relating to the particular media content item.
 9. The methodof claim 1, wherein the cluster groups together the certain mediacontent items further based on a relationship between at least oneannotation of each of the certain media content items, the relationshiprelating to at least one of geographical proximity, time proximity,similarity of caption terms, similarity of a place, similarity of avisual feature, similarity of an event, similarity of a metric, orsimilarity of a user statistic.
 10. The method of claim 1, wherein thegenerating the set of annotations for the particular media content itembased on metadata of the particular media content item comprises:identifying a place annotation for the particular media content itembased on geographic coordinates provided by the metadata.
 11. A systemcomprising: one or more processors; one or more machine-readable mediumsstoring instructions that, when executed by the one or more processors,cause the system to perform operations comprising: generating a set ofannotations for a particular media content item, in a plurality of mediacontent items, based on metadata of the particular media content item;associating the particular media content item with a cluster based onthe set of annotations generated for the particular media content item,the cluster grouping together certain media content items within theplurality of media content items based on a cohesiveness of geographiclocation and time; generating a collection of media content items fromthe plurality of media content items based on the cluster, thecollection comprising two or more media content items from the pluralityof media content items that are associated with the cluster, thecollection being separate from the cluster; determining a set ofattributes for the collection of media content items based on at leastone annotation of a media content item included in the collection ofmedia content items; storing, on a new index of a search service index,the collection of media content items in association with the set ofattributes, the storing comprising swapping a current index of thesearch service index with the new index such that a previous version ofthe collection stored on the current index is replaced by the collectionstored on the new index; searching, on the new index of the searchservice index, a plurality of collections of media content items basedon a search request from a client device to identify a set ofcollections of interest, the plurality of collections of media contentitems including the stored collection of media content items; andproviding search response to the client device based on the searching,the search response identifying the stored collection of media contentitems as part of the set of collections of interest.
 12. The system ofclaim 11, wherein the determining the collection of media content itemsfrom the plurality of media content items comprises determining theplurality of collections of media content items, the operations furthercomprising: reconciling the plurality of collections by merging at leasttwo collections of media content items, in the plurality of collections,based on a similarity between at least one attribute of each of the atleast two collections of media content items.
 13. The system of claim11, wherein the determining the collection of media content items fromthe plurality of media content items comprises determining the pluralityof collections of media content items, the operations furthercomprising: reconciling the plurality of collections by removing aparticular collection of media content items, from the plurality ofcollections, based on a similarity between at least one attribute of theparticular collection of media content items and another collection ofmedia content items in the plurality of collections.
 14. The system ofclaim 11, wherein the determining the collection of media content itemsfrom the plurality of media content items comprises determining theplurality of collections of media content items, the operations furthercomprising: filtering the plurality of collections of media contentitems to produce a filtered plurality of collections of media contentitems that includes media content items having at least a minimum set ofattributes.
 15. The system of claim 11, wherein the determining thecollection of media content items from the plurality of media contentitems comprises: selecting a highlight media content item, from thecollection of media content items, to represent the collection of mediacontent items based on a set of scores associated with the highlightmedia content item.
 16. The system of claim 11, wherein the set ofattributes include at least one of a caption associated with thecollection of media content items, a place name associated with thecollection of media content items, and a category associated with thecollection of media content items.
 17. The system of claim 11, whereinthe at least one annotation relates to a particular time, a particulargeographic location, a place, a topic, a visual feature, a caption, anevent, a media quality of the particular media content item, or a userstatistic relating to the particular media content item.
 18. The systemof claim 11, wherein the cluster groups together the certain mediacontent items further based on a relationship between at least oneannotation of each of the certain media content items, the relationshiprelating to at least one of geographical proximity, time proximity,similarity of caption terms, similarity of a place, similarity of avisual feature, similarity of an event, similarity of a metric, orsimilarity of a user statistic.
 19. The system of claim 11, wherein thegenerating the set of annotations for the particular media content itembased on metadata of the particular media content item comprises:identifying a place annotation for the particular media content itembased on geographic coordinates provided by the metadata.
 20. Anon-transitory machine-readable medium storing instructions that, whenexecuted by one or more computer processors, cause the one or moreprocessors to perform operations comprising: generating a set ofannotations for a particular media content item, in a plurality of mediacontent items, based on metadata of the particular media content item;associating the particular media content item with a cluster based onthe set of annotations generated for the particular media content item,the cluster grouping together certain media content items within theplurality of media content items based on a cohesiveness of geographiclocation and time; generating a collection of media content items fromthe plurality of media content items based on the cluster, thecollection comprising two or more media content items from the pluralityof media content items that are associated with the cluster, thecollection being separate from the cluster; determining a set ofattributes for the collection of media content items based on at leastone annotation of a media content item included in the collection ofmedia content items; storing, on a new index of a search service index,the collection of media content items in association with the set ofattributes, the storing comprising swapping a current index of thesearch service index with the new index such that a previous version ofthe collection stored on the current index is replaced by the collectionstored on the new index; searching, on the new index of the searchservice index, a plurality of collections of media content items basedon a search request from a client device to identify a set ofcollections of interest, the plurality of collections of media contentitems including the stored collection of media content items; andproviding search response to the client device based on the searching,the search response identifying the stored collection of media contentitems as part of the set of collections of interest.